Overview

Brought to you by YData

Dataset statistics

Number of variables89
Number of observations19
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory655.9 B

Variable types

Numeric42
Categorical38
Boolean9

Alerts

baronKills has constant value "0" Constant
detectorWardsPlaced has constant value "0" Constant
dragonKills has constant value "0" Constant
firstBloodAssist has constant value "False" Constant
firstBloodKill has constant value "False" Constant
firstTowerKill has constant value "False" Constant
gameEndedInEarlySurrender has constant value "False" Constant
objectivesStolenAssists has constant value "0" Constant
pentaKills has constant value "0" Constant
placement has constant value "0" Constant
quadraKills has constant value "0" Constant
sightWardsBoughtInGame has constant value "0" Constant
subteamPlacement has constant value "0" Constant
summoner1Id has constant value "12" Constant
teamEarlySurrendered has constant value "False" Constant
teamPosition has constant value "TOP" Constant
totalAllyJungleMinionsKilled has constant value "0" Constant
totalEnemyJungleMinionsKilled has constant value "0" Constant
totalHealsOnTeammates has constant value "0" Constant
totalUnitsHealed has constant value "1" Constant
unrealKills has constant value "0" Constant
visionClearedPings has constant value "0" Constant
visionWardsBoughtInGame has constant value "0" Constant
assists is highly overall correlated with objectivesStolen and 5 other fieldsHigh correlation
bountyLevel is highly overall correlated with inhibitorTakedowns and 5 other fieldsHigh correlation
champLevel is highly overall correlated with damageSelfMitigated and 32 other fieldsHigh correlation
championId is highly overall correlated with championName and 7 other fieldsHigh correlation
championName is highly overall correlated with championId and 7 other fieldsHigh correlation
damageDealtToBuildings is highly overall correlated with damageDealtToObjectives and 14 other fieldsHigh correlation
damageDealtToObjectives is highly overall correlated with damageDealtToBuildings and 12 other fieldsHigh correlation
damageDealtToTurrets is highly overall correlated with damageDealtToBuildings and 14 other fieldsHigh correlation
damageSelfMitigated is highly overall correlated with champLevel and 22 other fieldsHigh correlation
deaths is highly overall correlated with champLevel and 21 other fieldsHigh correlation
doubleKills is highly overall correlated with largestMultiKillHigh correlation
eligibleForProgression is highly overall correlated with champLevel and 12 other fieldsHigh correlation
firstTowerAssist is highly overall correlated with champLevel and 3 other fieldsHigh correlation
gameEndedInSurrender is highly overall correlated with totalDamageDealtToChampions and 1 other fieldsHigh correlation
goldEarned is highly overall correlated with champLevel and 29 other fieldsHigh correlation
goldSpent is highly overall correlated with champLevel and 28 other fieldsHigh correlation
inhibitorKills is highly overall correlated with champLevel and 5 other fieldsHigh correlation
inhibitorTakedowns is highly overall correlated with bountyLevel and 10 other fieldsHigh correlation
inhibitorsLost is highly overall correlated with nexusLost and 1 other fieldsHigh correlation
itemsPurchased is highly overall correlated with champLevel and 24 other fieldsHigh correlation
killingSprees is highly overall correlated with largestKillingSpreeHigh correlation
kills is highly overall correlated with bountyLevel and 5 other fieldsHigh correlation
lane is highly overall correlated with largestKillingSpree and 2 other fieldsHigh correlation
largestKillingSpree is highly overall correlated with bountyLevel and 7 other fieldsHigh correlation
largestMultiKill is highly overall correlated with bountyLevel and 8 other fieldsHigh correlation
longestTimeSpentLiving is highly overall correlated with summoner2Id and 1 other fieldsHigh correlation
magicDamageDealt is highly overall correlated with champLevel and 26 other fieldsHigh correlation
magicDamageDealtToChampions is highly overall correlated with champLevel and 27 other fieldsHigh correlation
magicDamageTaken is highly overall correlated with damageSelfMitigated and 14 other fieldsHigh correlation
neutralMinionsKilled is highly overall correlated with objectivesStolen and 1 other fieldsHigh correlation
nexusKills is highly overall correlated with damageDealtToBuildings and 10 other fieldsHigh correlation
nexusLost is highly overall correlated with inhibitorTakedowns and 6 other fieldsHigh correlation
nexusTakedowns is highly overall correlated with damageDealtToObjectives and 8 other fieldsHigh correlation
objectivesStolen is highly overall correlated with assists and 2 other fieldsHigh correlation
physicalDamageDealt is highly overall correlated with champLevel and 27 other fieldsHigh correlation
physicalDamageDealtToChampions is highly overall correlated with assists and 15 other fieldsHigh correlation
physicalDamageTaken is highly overall correlated with champLevel and 28 other fieldsHigh correlation
role is highly overall correlated with goldSpent and 3 other fieldsHigh correlation
spell1Casts is highly overall correlated with champLevel and 25 other fieldsHigh correlation
spell2Casts is highly overall correlated with champLevel and 24 other fieldsHigh correlation
spell3Casts is highly overall correlated with champLevel and 33 other fieldsHigh correlation
spell4Casts is highly overall correlated with champLevel and 19 other fieldsHigh correlation
summoner1Casts is highly overall correlated with eligibleForProgression and 2 other fieldsHigh correlation
summoner2Casts is highly overall correlated with champLevel and 26 other fieldsHigh correlation
summoner2Id is highly overall correlated with championId and 12 other fieldsHigh correlation
timeCCingOthers is highly overall correlated with assists and 6 other fieldsHigh correlation
timePlayed is highly overall correlated with champLevel and 27 other fieldsHigh correlation
totalDamageDealt is highly overall correlated with champLevel and 27 other fieldsHigh correlation
totalDamageDealtToChampions is highly overall correlated with champLevel and 30 other fieldsHigh correlation
totalDamageShieldedOnTeammates is highly overall correlated with assists and 4 other fieldsHigh correlation
totalDamageTaken is highly overall correlated with champLevel and 30 other fieldsHigh correlation
totalHeal is highly overall correlated with champLevel and 27 other fieldsHigh correlation
totalMinionsKilled is highly overall correlated with champLevel and 24 other fieldsHigh correlation
totalTimeCCDealt is highly overall correlated with champLevel and 22 other fieldsHigh correlation
totalTimeSpentDead is highly overall correlated with champLevel and 22 other fieldsHigh correlation
tripleKills is highly overall correlated with bountyLevel and 5 other fieldsHigh correlation
trueDamageDealt is highly overall correlated with championId and 6 other fieldsHigh correlation
trueDamageDealtToChampions is highly overall correlated with gameEndedInSurrender and 3 other fieldsHigh correlation
trueDamageTaken is highly overall correlated with champLevel and 16 other fieldsHigh correlation
turretKills is highly overall correlated with damageDealtToBuildings and 11 other fieldsHigh correlation
turretTakedowns is highly overall correlated with champLevel and 17 other fieldsHigh correlation
turretsLost is highly overall correlated with nexusLost and 2 other fieldsHigh correlation
visionScore is highly overall correlated with assists and 16 other fieldsHigh correlation
wardsKilled is highly overall correlated with assists and 2 other fieldsHigh correlation
wardsPlaced is highly overall correlated with champLevel and 22 other fieldsHigh correlation
win is highly overall correlated with inhibitorTakedowns and 6 other fieldsHigh correlation
eligibleForProgression is highly imbalanced (70.3%) Imbalance
firstTowerAssist is highly imbalanced (70.3%) Imbalance
inhibitorKills is highly imbalanced (51.5%) Imbalance
nexusKills is highly imbalanced (70.3%) Imbalance
objectivesStolen is highly imbalanced (51.5%) Imbalance
tripleKills is highly imbalanced (70.3%) Imbalance
damageSelfMitigated has unique values Unique
goldEarned has unique values Unique
magicDamageDealt has unique values Unique
magicDamageDealtToChampions has unique values Unique
magicDamageTaken has unique values Unique
physicalDamageDealt has unique values Unique
physicalDamageDealtToChampions has unique values Unique
physicalDamageTaken has unique values Unique
spell1Casts has unique values Unique
spell3Casts has unique values Unique
totalDamageDealt has unique values Unique
totalDamageDealtToChampions has unique values Unique
totalDamageTaken has unique values Unique
totalHeal has unique values Unique
totalMinionsKilled has unique values Unique
trueDamageDealt has unique values Unique
trueDamageTaken has unique values Unique
assists has 1 (5.3%) zeros Zeros
bountyLevel has 2 (10.5%) zeros Zeros
damageDealtToBuildings has 2 (10.5%) zeros Zeros
damageDealtToObjectives has 2 (10.5%) zeros Zeros
damageDealtToTurrets has 2 (10.5%) zeros Zeros
deaths has 2 (10.5%) zeros Zeros
kills has 2 (10.5%) zeros Zeros
largestKillingSpree has 7 (36.8%) zeros Zeros
longestTimeSpentLiving has 2 (10.5%) zeros Zeros
magicDamageDealt has 1 (5.3%) zeros Zeros
magicDamageDealtToChampions has 1 (5.3%) zeros Zeros
magicDamageTaken has 1 (5.3%) zeros Zeros
summoner2Casts has 1 (5.3%) zeros Zeros
totalDamageShieldedOnTeammates has 8 (42.1%) zeros Zeros
totalTimeSpentDead has 2 (10.5%) zeros Zeros
trueDamageDealtToChampions has 3 (15.8%) zeros Zeros
trueDamageTaken has 1 (5.3%) zeros Zeros
turretTakedowns has 8 (42.1%) zeros Zeros
turretsLost has 2 (10.5%) zeros Zeros

Reproduction

Analysis started2025-03-06 09:37:01.549570
Analysis finished2025-03-06 09:38:38.644686
Duration1 minute and 37.1 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

assists
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1052632
Minimum0
Maximum25
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:38.682283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q15.5
median8
Q312
95-th percentile21.4
Maximum25
Range25
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation6.3236306
Coefficient of variation (CV)0.69450278
Kurtosis1.2681632
Mean9.1052632
Median Absolute Deviation (MAD)3
Skewness1.0172171
Sum173
Variance39.988304
MonotonicityNot monotonic
2025-03-06T10:38:38.730791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
9 3
15.8%
8 2
10.5%
6 2
10.5%
5 2
10.5%
14 2
10.5%
21 1
 
5.3%
25 1
 
5.3%
11 1
 
5.3%
1 1
 
5.3%
13 1
 
5.3%
Other values (3) 3
15.8%
ValueCountFrequency (%)
0 1
 
5.3%
1 1
 
5.3%
2 1
 
5.3%
5 2
10.5%
6 2
10.5%
7 1
 
5.3%
8 2
10.5%
9 3
15.8%
11 1
 
5.3%
13 1
 
5.3%
ValueCountFrequency (%)
25 1
 
5.3%
21 1
 
5.3%
14 2
10.5%
13 1
 
5.3%
11 1
 
5.3%
9 3
15.8%
8 2
10.5%
7 1
 
5.3%
6 2
10.5%
5 2
10.5%

baronKills
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:38.787799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:38.830018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

bountyLevel
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9473684
Minimum0
Maximum12
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:38.859030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35.5
95-th percentile6.6
Maximum12
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.8377159
Coefficient of variation (CV)0.71888803
Kurtosis2.3494031
Mean3.9473684
Median Absolute Deviation (MAD)2
Skewness1.0050718
Sum75
Variance8.0526316
MonotonicityNot monotonic
2025-03-06T10:38:38.906557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 4
21.1%
6 4
21.1%
3 3
15.8%
1 2
10.5%
0 2
10.5%
2 2
10.5%
12 1
 
5.3%
4 1
 
5.3%
ValueCountFrequency (%)
0 2
10.5%
1 2
10.5%
2 2
10.5%
3 3
15.8%
4 1
 
5.3%
5 4
21.1%
6 4
21.1%
12 1
 
5.3%
ValueCountFrequency (%)
12 1
 
5.3%
6 4
21.1%
5 4
21.1%
4 1
 
5.3%
3 3
15.8%
2 2
10.5%
1 2
10.5%
0 2
10.5%

champLevel
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum8
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:38.953555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10.7
Q114
median16
Q316
95-th percentile18
Maximum18
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5166115
Coefficient of variation (CV)0.1677741
Kurtosis2.2430341
Mean15
Median Absolute Deviation (MAD)1
Skewness-1.3323237
Sum285
Variance6.3333333
MonotonicityNot monotonic
2025-03-06T10:38:39.001604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
16 6
31.6%
14 3
15.8%
18 3
15.8%
15 3
15.8%
11 1
 
5.3%
12 1
 
5.3%
8 1
 
5.3%
17 1
 
5.3%
ValueCountFrequency (%)
8 1
 
5.3%
11 1
 
5.3%
12 1
 
5.3%
14 3
15.8%
15 3
15.8%
16 6
31.6%
17 1
 
5.3%
18 3
15.8%
ValueCountFrequency (%)
18 3
15.8%
17 1
 
5.3%
16 6
31.6%
15 3
15.8%
14 3
15.8%
12 1
 
5.3%
11 1
 
5.3%
8 1
 
5.3%

championId
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
98
11 
36
6
 
1
54
 
1

Length

Max length2
Median length2
Mean length1.9473684
Min length1

Characters and Unicode

Total characters37
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st row98
2nd row98
3rd row98
4th row98
5th row98

Common Values

ValueCountFrequency (%)
98 11
57.9%
36 6
31.6%
6 1
 
5.3%
54 1
 
5.3%

Length

2025-03-06T10:38:39.059108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:39.102622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98 11
57.9%
36 6
31.6%
6 1
 
5.3%
54 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
9 11
29.7%
8 11
29.7%
6 7
18.9%
3 6
16.2%
5 1
 
2.7%
4 1
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 11
29.7%
8 11
29.7%
6 7
18.9%
3 6
16.2%
5 1
 
2.7%
4 1
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 11
29.7%
8 11
29.7%
6 7
18.9%
3 6
16.2%
5 1
 
2.7%
4 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 11
29.7%
8 11
29.7%
6 7
18.9%
3 6
16.2%
5 1
 
2.7%
4 1
 
2.7%

championName
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
Shen
11 
DrMundo
Urgot
 
1
Malphite
 
1

Length

Max length8
Median length4
Mean length5.2105263
Min length4

Characters and Unicode

Total characters99
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st rowShen
2nd rowShen
3rd rowShen
4th rowShen
5th rowShen

Common Values

ValueCountFrequency (%)
Shen 11
57.9%
DrMundo 6
31.6%
Urgot 1
 
5.3%
Malphite 1
 
5.3%

Length

2025-03-06T10:38:39.156620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:39.202139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
shen 11
57.9%
drmundo 6
31.6%
urgot 1
 
5.3%
malphite 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
n 17
17.2%
e 12
12.1%
h 12
12.1%
S 11
11.1%
r 7
7.1%
M 7
7.1%
o 7
7.1%
u 6
 
6.1%
D 6
 
6.1%
d 6
 
6.1%
Other values (7) 8
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 17
17.2%
e 12
12.1%
h 12
12.1%
S 11
11.1%
r 7
7.1%
M 7
7.1%
o 7
7.1%
u 6
 
6.1%
D 6
 
6.1%
d 6
 
6.1%
Other values (7) 8
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 17
17.2%
e 12
12.1%
h 12
12.1%
S 11
11.1%
r 7
7.1%
M 7
7.1%
o 7
7.1%
u 6
 
6.1%
D 6
 
6.1%
d 6
 
6.1%
Other values (7) 8
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 17
17.2%
e 12
12.1%
h 12
12.1%
S 11
11.1%
r 7
7.1%
M 7
7.1%
o 7
7.1%
u 6
 
6.1%
D 6
 
6.1%
d 6
 
6.1%
Other values (7) 8
8.1%

damageDealtToBuildings
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3368.9474
Minimum0
Maximum13773
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:39.247140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1585.5
median1491
Q34037
95-th percentile11782.2
Maximum13773
Range13773
Interquartile range (IQR)3451.5

Descriptive statistics

Standard deviation4172.3754
Coefficient of variation (CV)1.2384804
Kurtosis1.5970527
Mean3368.9474
Median Absolute Deviation (MAD)1438
Skewness1.6116164
Sum64010
Variance17408717
MonotonicityNot monotonic
2025-03-06T10:38:39.300665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
10.5%
1291 1
 
5.3%
1357 1
 
5.3%
1491 1
 
5.3%
627 1
 
5.3%
927 1
 
5.3%
3701 1
 
5.3%
53 1
 
5.3%
544 1
 
5.3%
2315 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0 2
10.5%
53 1
5.3%
233 1
5.3%
544 1
5.3%
627 1
5.3%
927 1
5.3%
1291 1
5.3%
1357 1
5.3%
1491 1
5.3%
2315 1
5.3%
ValueCountFrequency (%)
13773 1
5.3%
11561 1
5.3%
10941 1
5.3%
4447 1
5.3%
4151 1
5.3%
3923 1
5.3%
3701 1
5.3%
2675 1
5.3%
2315 1
5.3%
1491 1
5.3%

damageDealtToObjectives
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5474.1053
Minimum0
Maximum16712
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:39.354660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12474.5
median4110
Q35745
95-th percentile15698.6
Maximum16712
Range16712
Interquartile range (IQR)3270.5

Descriptive statistics

Standard deviation5208.0841
Coefficient of variation (CV)0.95140371
Kurtosis0.28753767
Mean5474.1053
Median Absolute Deviation (MAD)1692
Skewness1.1824297
Sum104008
Variance27124140
MonotonicityNot monotonic
2025-03-06T10:38:39.408172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
10.5%
13975 1
 
5.3%
5802 1
 
5.3%
4996 1
 
5.3%
627 1
 
5.3%
2634 1
 
5.3%
5035 1
 
5.3%
3505 1
 
5.3%
622 1
 
5.3%
2315 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0 2
10.5%
622 1
5.3%
627 1
5.3%
2315 1
5.3%
2634 1
5.3%
2708 1
5.3%
3255 1
5.3%
3505 1
5.3%
4110 1
5.3%
4447 1
5.3%
ValueCountFrequency (%)
16712 1
5.3%
15586 1
5.3%
13975 1
5.3%
11991 1
5.3%
5802 1
5.3%
5688 1
5.3%
5035 1
5.3%
4996 1
5.3%
4447 1
5.3%
4110 1
5.3%

damageDealtToTurrets
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3368.9474
Minimum0
Maximum13773
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:39.457172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1585.5
median1491
Q34037
95-th percentile11782.2
Maximum13773
Range13773
Interquartile range (IQR)3451.5

Descriptive statistics

Standard deviation4172.3754
Coefficient of variation (CV)1.2384804
Kurtosis1.5970527
Mean3368.9474
Median Absolute Deviation (MAD)1438
Skewness1.6116164
Sum64010
Variance17408717
MonotonicityNot monotonic
2025-03-06T10:38:39.510683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
10.5%
1291 1
 
5.3%
1357 1
 
5.3%
1491 1
 
5.3%
627 1
 
5.3%
927 1
 
5.3%
3701 1
 
5.3%
53 1
 
5.3%
544 1
 
5.3%
2315 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0 2
10.5%
53 1
5.3%
233 1
5.3%
544 1
5.3%
627 1
5.3%
927 1
5.3%
1291 1
5.3%
1357 1
5.3%
1491 1
5.3%
2315 1
5.3%
ValueCountFrequency (%)
13773 1
5.3%
11561 1
5.3%
10941 1
5.3%
4447 1
5.3%
4151 1
5.3%
3923 1
5.3%
3701 1
5.3%
2675 1
5.3%
2315 1
5.3%
1491 1
5.3%

damageSelfMitigated
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35188.526
Minimum2398
Maximum88228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:39.566685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2398
5-th percentile7699.9
Q123082.5
median29344
Q342402.5
95-th percentile73903.6
Maximum88228
Range85830
Interquartile range (IQR)19320

Descriptive statistics

Standard deviation21304.212
Coefficient of variation (CV)0.60543065
Kurtosis1.0498229
Mean35188.526
Median Absolute Deviation (MAD)10546
Skewness0.98738551
Sum668582
Variance4.5386947 × 108
MonotonicityNot monotonic
2025-03-06T10:38:39.620196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
39637 1
 
5.3%
27539 1
 
5.3%
8289 1
 
5.3%
88228 1
 
5.3%
40701 1
 
5.3%
17543 1
 
5.3%
2398 1
 
5.3%
30113 1
 
5.3%
23041 1
 
5.3%
44104 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
2398 1
5.3%
8289 1
5.3%
17543 1
5.3%
20379 1
5.3%
23041 1
5.3%
23124 1
5.3%
24514 1
5.3%
26849 1
5.3%
27539 1
5.3%
29344 1
5.3%
ValueCountFrequency (%)
88228 1
5.3%
72312 1
5.3%
62144 1
5.3%
48433 1
5.3%
44104 1
5.3%
40701 1
5.3%
39890 1
5.3%
39637 1
5.3%
30113 1
5.3%
29344 1
5.3%

deaths
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum0
Maximum8
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:39.668196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median4
Q36
95-th percentile7.1
Maximum8
Range8
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3804761
Coefficient of variation (CV)0.59511904
Kurtosis-0.89059638
Mean4
Median Absolute Deviation (MAD)2
Skewness-0.27617981
Sum76
Variance5.6666667
MonotonicityNot monotonic
2025-03-06T10:38:39.717713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 5
26.3%
4 4
21.1%
0 2
 
10.5%
3 2
 
10.5%
1 2
 
10.5%
2 1
 
5.3%
8 1
 
5.3%
5 1
 
5.3%
7 1
 
5.3%
ValueCountFrequency (%)
0 2
 
10.5%
1 2
 
10.5%
2 1
 
5.3%
3 2
 
10.5%
4 4
21.1%
5 1
 
5.3%
6 5
26.3%
7 1
 
5.3%
8 1
 
5.3%
ValueCountFrequency (%)
8 1
 
5.3%
7 1
 
5.3%
6 5
26.3%
5 1
 
5.3%
4 4
21.1%
3 2
 
10.5%
2 1
 
5.3%
1 2
 
10.5%
0 2
 
10.5%

detectorWardsPlaced
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:39.776713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:39.810226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

doubleKills
Categorical

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
14 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

Length

2025-03-06T10:38:39.851226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:39.889238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

Most occurring characters

ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 5
 
26.3%

dragonKills
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:39.937744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:39.975744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

eligibleForProgression
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
18 
False
 
1
ValueCountFrequency (%)
True 18
94.7%
False 1
 
5.3%
2025-03-06T10:38:39.996250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

firstBloodAssist
Boolean

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
ValueCountFrequency (%)
False 19
100.0%
2025-03-06T10:38:40.018257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

firstBloodKill
Boolean

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
ValueCountFrequency (%)
False 19
100.0%
2025-03-06T10:38:40.037254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

firstTowerAssist
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
18 
True
 
1
ValueCountFrequency (%)
False 18
94.7%
True 1
 
5.3%
2025-03-06T10:38:40.057255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

firstTowerKill
Boolean

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
ValueCountFrequency (%)
False 19
100.0%
2025-03-06T10:38:40.077254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

gameEndedInEarlySurrender
Boolean

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
ValueCountFrequency (%)
False 19
100.0%
2025-03-06T10:38:40.095760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

gameEndedInSurrender
Boolean

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
11 
True
ValueCountFrequency (%)
False 11
57.9%
True 8
42.1%
2025-03-06T10:38:40.118766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

goldEarned
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9549.2105
Minimum3292
Maximum13430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:40.157640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3292
5-th percentile5515.9
Q19003
median9401
Q310775
95-th percentile12694.7
Maximum13430
Range10138
Interquartile range (IQR)1772

Descriptive statistics

Standard deviation2388.3484
Coefficient of variation (CV)0.25010952
Kurtosis1.6074532
Mean9549.2105
Median Absolute Deviation (MAD)1341
Skewness-0.83780854
Sum181435
Variance5704208.3
MonotonicityNot monotonic
2025-03-06T10:38:40.209152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10030 1
 
5.3%
7806 1
 
5.3%
5763 1
 
5.3%
12613 1
 
5.3%
9303 1
 
5.3%
7749 1
 
5.3%
3292 1
 
5.3%
9401 1
 
5.3%
8955 1
 
5.3%
9202 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
3292 1
5.3%
5763 1
5.3%
7749 1
5.3%
7806 1
5.3%
8955 1
5.3%
9051 1
5.3%
9115 1
5.3%
9202 1
5.3%
9303 1
5.3%
9401 1
5.3%
ValueCountFrequency (%)
13430 1
5.3%
12613 1
5.3%
12354 1
5.3%
11921 1
5.3%
10808 1
5.3%
10742 1
5.3%
10406 1
5.3%
10030 1
5.3%
9494 1
5.3%
9401 1
5.3%

goldSpent
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8687.3684
Minimum2200
Maximum12750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:40.261153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4450
Q18065
median8900
Q39975
95-th percentile12030
Maximum12750
Range10550
Interquartile range (IQR)1910

Descriptive statistics

Standard deviation2577.6041
Coefficient of variation (CV)0.29670712
Kurtosis1.2336535
Mean8687.3684
Median Absolute Deviation (MAD)870
Skewness-0.9556629
Sum165060
Variance6644042.7
MonotonicityNot monotonic
2025-03-06T10:38:40.315670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
9750 2
 
10.5%
7400 1
 
5.3%
4700 1
 
5.3%
12750 1
 
5.3%
8850 1
 
5.3%
4750 1
 
5.3%
2200 1
 
5.3%
8650 1
 
5.3%
8030 1
 
5.3%
8700 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
2200 1
5.3%
4700 1
5.3%
4750 1
5.3%
7400 1
5.3%
8030 1
5.3%
8100 1
5.3%
8650 1
5.3%
8700 1
5.3%
8850 1
5.3%
8900 1
5.3%
ValueCountFrequency (%)
12750 1
5.3%
11950 1
5.3%
11450 1
5.3%
10650 1
5.3%
10200 1
5.3%
9750 2
10.5%
9200 1
5.3%
9080 1
5.3%
8900 1
5.3%
8850 1
5.3%

inhibitorKills
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
17 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Length

2025-03-06T10:38:40.376179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:40.412700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

inhibitorTakedowns
Categorical

High correlation 

Distinct3
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
14 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

Length

2025-03-06T10:38:40.459697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:40.597730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14
73.7%
1 4
 
21.1%
3 1
 
5.3%

inhibitorsLost
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
2
1
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row3
5th row1

Common Values

ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

Length

2025-03-06T10:38:40.646731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:40.688731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9
47.4%
2 5
26.3%
1 3
 
15.8%
3 2
 
10.5%

itemsPurchased
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.105263
Minimum4
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:40.733241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.8
Q114.5
median16
Q319
95-th percentile23.6
Maximum29
Range25
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.8679424
Coefficient of variation (CV)0.36434937
Kurtosis0.85180938
Mean16.105263
Median Absolute Deviation (MAD)3
Skewness-0.18492143
Sum306
Variance34.432749
MonotonicityNot monotonic
2025-03-06T10:38:40.785243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15 3
15.8%
16 3
15.8%
19 3
15.8%
21 2
10.5%
14 1
 
5.3%
8 1
 
5.3%
4 1
 
5.3%
6 1
 
5.3%
13 1
 
5.3%
17 1
 
5.3%
Other values (2) 2
10.5%
ValueCountFrequency (%)
4 1
 
5.3%
6 1
 
5.3%
8 1
 
5.3%
13 1
 
5.3%
14 1
 
5.3%
15 3
15.8%
16 3
15.8%
17 1
 
5.3%
19 3
15.8%
21 2
10.5%
ValueCountFrequency (%)
29 1
 
5.3%
23 1
 
5.3%
21 2
10.5%
19 3
15.8%
17 1
 
5.3%
16 3
15.8%
15 3
15.8%
14 1
 
5.3%
13 1
 
5.3%
8 1
 
5.3%

killingSprees
Categorical

High correlation 

Distinct3
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size284.0 B
1
0
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

Length

2025-03-06T10:38:40.842757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:40.880760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

Most occurring characters

ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9
47.4%
0 7
36.8%
2 3
 
15.8%

kills
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9473684
Minimum0
Maximum12
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:40.920799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35.5
95-th percentile6.6
Maximum12
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.8377159
Coefficient of variation (CV)0.71888803
Kurtosis2.3494031
Mean3.9473684
Median Absolute Deviation (MAD)2
Skewness1.0050718
Sum75
Variance8.0526316
MonotonicityNot monotonic
2025-03-06T10:38:40.969797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 4
21.1%
6 4
21.1%
3 3
15.8%
1 2
10.5%
0 2
10.5%
2 2
10.5%
12 1
 
5.3%
4 1
 
5.3%
ValueCountFrequency (%)
0 2
10.5%
1 2
10.5%
2 2
10.5%
3 3
15.8%
4 1
 
5.3%
5 4
21.1%
6 4
21.1%
12 1
 
5.3%
ValueCountFrequency (%)
12 1
 
5.3%
6 4
21.1%
5 4
21.1%
4 1
 
5.3%
3 3
15.8%
2 2
10.5%
1 2
10.5%
0 2
10.5%

lane
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
TOP
13 
NONE
JUNGLE
MIDDLE
 
1

Length

Max length6
Median length3
Mean length3.6315789
Min length3

Characters and Unicode

Total characters69
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st rowTOP
2nd rowTOP
3rd rowNONE
4th rowTOP
5th rowJUNGLE

Common Values

ValueCountFrequency (%)
TOP 13
68.4%
NONE 3
 
15.8%
JUNGLE 2
 
10.5%
MIDDLE 1
 
5.3%

Length

2025-03-06T10:38:41.027312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.071316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
top 13
68.4%
none 3
 
15.8%
jungle 2
 
10.5%
middle 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
O 16
23.2%
T 13
18.8%
P 13
18.8%
N 8
11.6%
E 6
 
8.7%
L 3
 
4.3%
U 2
 
2.9%
J 2
 
2.9%
G 2
 
2.9%
D 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 16
23.2%
T 13
18.8%
P 13
18.8%
N 8
11.6%
E 6
 
8.7%
L 3
 
4.3%
U 2
 
2.9%
J 2
 
2.9%
G 2
 
2.9%
D 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 16
23.2%
T 13
18.8%
P 13
18.8%
N 8
11.6%
E 6
 
8.7%
L 3
 
4.3%
U 2
 
2.9%
J 2
 
2.9%
G 2
 
2.9%
D 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 16
23.2%
T 13
18.8%
P 13
18.8%
N 8
11.6%
E 6
 
8.7%
L 3
 
4.3%
U 2
 
2.9%
J 2
 
2.9%
G 2
 
2.9%
D 2
 
2.9%
Other values (2) 2
 
2.9%

largestKillingSpree
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2631579
Minimum0
Maximum11
Zeros7
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:41.112843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5.6
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6424338
Coefficient of variation (CV)1.167587
Kurtosis6.1006219
Mean2.2631579
Median Absolute Deviation (MAD)2
Skewness2.0643551
Sum43
Variance6.9824561
MonotonicityNot monotonic
2025-03-06T10:38:41.154840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7
36.8%
3 5
26.3%
2 4
21.1%
4 1
 
5.3%
5 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
0 7
36.8%
2 4
21.1%
3 5
26.3%
4 1
 
5.3%
5 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
11 1
 
5.3%
5 1
 
5.3%
4 1
 
5.3%
3 5
26.3%
2 4
21.1%
0 7
36.8%

largestMultiKill
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
1
12 
2
0
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

Length

2025-03-06T10:38:41.209366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.252366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12
63.2%
2 4
 
21.1%
0 2
 
10.5%
3 1
 
5.3%

longestTimeSpentLiving
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean731.36842
Minimum0
Maximum1620
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:41.295874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1573.5
median665
Q31011.5
95-th percentile1233
Maximum1620
Range1620
Interquartile range (IQR)438

Descriptive statistics

Standard deviation398.17699
Coefficient of variation (CV)0.54442737
Kurtosis0.49865988
Mean731.36842
Median Absolute Deviation (MAD)244
Skewness0.084337594
Sum13896
Variance158544.91
MonotonicityNot monotonic
2025-03-06T10:38:41.348880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
10.5%
609 1
 
5.3%
647 1
 
5.3%
954 1
 
5.3%
909 1
 
5.3%
803 1
 
5.3%
1620 1
 
5.3%
1094 1
 
5.3%
1190 1
 
5.3%
1104 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0 2
10.5%
384 1
5.3%
421 1
5.3%
558 1
5.3%
589 1
5.3%
602 1
5.3%
609 1
5.3%
647 1
5.3%
665 1
5.3%
678 1
5.3%
ValueCountFrequency (%)
1620 1
5.3%
1190 1
5.3%
1104 1
5.3%
1094 1
5.3%
1069 1
5.3%
954 1
5.3%
909 1
5.3%
803 1
5.3%
678 1
5.3%
665 1
5.3%

magicDamageDealt
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28848.947
Minimum0
Maximum88048
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:41.402392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2244.6
Q115785
median22889
Q341008.5
95-th percentile52199.2
Maximum88048
Range88048
Interquartile range (IQR)25223.5

Descriptive statistics

Standard deviation20688.455
Coefficient of variation (CV)0.71713034
Kurtosis2.4311102
Mean28848.947
Median Absolute Deviation (MAD)14784
Skewness1.1410704
Sum548130
Variance4.2801219 × 108
MonotonicityNot monotonic
2025-03-06T10:38:41.452393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
22351 1
 
5.3%
13584 1
 
5.3%
8105 1
 
5.3%
46169 1
 
5.3%
18689 1
 
5.3%
10181 1
 
5.3%
2494 1
 
5.3%
22889 1
 
5.3%
20727 1
 
5.3%
42642 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0 1
5.3%
2494 1
5.3%
8105 1
5.3%
10181 1
5.3%
13584 1
5.3%
17986 1
5.3%
18689 1
5.3%
20727 1
5.3%
22351 1
5.3%
22889 1
5.3%
ValueCountFrequency (%)
88048 1
5.3%
48216 1
5.3%
46169 1
5.3%
42642 1
5.3%
41691 1
5.3%
40326 1
5.3%
39133 1
5.3%
33554 1
5.3%
31345 1
5.3%
22889 1
5.3%

magicDamageDealtToChampions
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9409.8947
Minimum0
Maximum22675
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:41.501990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1035
Q15062.5
median8932
Q313252
95-th percentile19031.8
Maximum22675
Range22675
Interquartile range (IQR)8189.5

Descriptive statistics

Standard deviation5918.4615
Coefficient of variation (CV)0.6289615
Kurtosis-0.024951
Mean9409.8947
Median Absolute Deviation (MAD)4403
Skewness0.477134
Sum178788
Variance35028187
MonotonicityNot monotonic
2025-03-06T10:38:41.555988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10300 1
 
5.3%
3544 1
 
5.3%
3867 1
 
5.3%
8932 1
 
5.3%
9245 1
 
5.3%
5989 1
 
5.3%
1150 1
 
5.3%
4529 1
 
5.3%
5596 1
 
5.3%
11424 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0 1
5.3%
1150 1
5.3%
3544 1
5.3%
3867 1
5.3%
4529 1
5.3%
5596 1
5.3%
5989 1
5.3%
7873 1
5.3%
8822 1
5.3%
8932 1
5.3%
ValueCountFrequency (%)
22675 1
5.3%
18627 1
5.3%
15503 1
5.3%
14208 1
5.3%
13507 1
5.3%
12997 1
5.3%
11424 1
5.3%
10300 1
5.3%
9245 1
5.3%
8932 1
5.3%

magicDamageTaken
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11598.684
Minimum0
Maximum28719
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:41.607499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1568.7
Q15848
median10688
Q314470.5
95-th percentile25796.7
Maximum28719
Range28719
Interquartile range (IQR)8622.5

Descriptive statistics

Standard deviation7687.456
Coefficient of variation (CV)0.66278691
Kurtosis0.24235405
Mean11598.684
Median Absolute Deviation (MAD)4788
Skewness0.72900378
Sum220375
Variance59096980
MonotonicityNot monotonic
2025-03-06T10:38:41.661502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13465 1
 
5.3%
10631 1
 
5.3%
1743 1
 
5.3%
9552 1
 
5.3%
11948 1
 
5.3%
6929 1
 
5.3%
0 1
 
5.3%
12051 1
 
5.3%
11993 1
 
5.3%
15476 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0 1
5.3%
1743 1
5.3%
4180 1
5.3%
4532 1
5.3%
4767 1
5.3%
6929 1
5.3%
8905 1
5.3%
9552 1
5.3%
10631 1
5.3%
10688 1
5.3%
ValueCountFrequency (%)
28719 1
5.3%
25472 1
5.3%
21987 1
5.3%
17337 1
5.3%
15476 1
5.3%
13465 1
5.3%
12051 1
5.3%
11993 1
5.3%
11948 1
5.3%
10688 1
5.3%

neutralMinionsKilled
Categorical

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
16 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row4
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

Length

2025-03-06T10:38:41.719531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.756529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16
84.2%
4 3
 
15.8%

nexusKills
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
18 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Length

2025-03-06T10:38:41.803050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.839049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

nexusLost
Categorical

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
1
11 
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

Length

2025-03-06T10:38:41.880049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.917562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

Most occurring characters

ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11
57.9%
0 8
42.1%

nexusTakedowns
Categorical

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
13 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

Length

2025-03-06T10:38:41.962560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:41.998074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

Most occurring characters

ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13
68.4%
1 6
31.6%

objectivesStolen
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
17 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Length

2025-03-06T10:38:42.042071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.077071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17
89.5%
1 2
 
10.5%

objectivesStolenAssists
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:42.121583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.154588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

pentaKills
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:42.192590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.226103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

physicalDamageDealt
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54722.842
Minimum8980
Maximum138251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:42.258103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8980
5-th percentile18158.2
Q131962
median39127
Q372199
95-th percentile136157.6
Maximum138251
Range129271
Interquartile range (IQR)40237

Descriptive statistics

Standard deviation36846.974
Coefficient of variation (CV)0.67333809
Kurtosis0.80211455
Mean54722.842
Median Absolute Deviation (MAD)14700
Skewness1.2148434
Sum1039734
Variance1.3576995 × 109
MonotonicityNot monotonic
2025-03-06T10:38:42.310615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
36064 1
 
5.3%
27884 1
 
5.3%
19178 1
 
5.3%
39127 1
 
5.3%
29112 1
 
5.3%
24427 1
 
5.3%
8980 1
 
5.3%
35435 1
 
5.3%
34812 1
 
5.3%
41108 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
8980 1
5.3%
19178 1
5.3%
24427 1
5.3%
27884 1
5.3%
29112 1
5.3%
34812 1
5.3%
35435 1
5.3%
36064 1
5.3%
37594 1
5.3%
39127 1
5.3%
ValueCountFrequency (%)
138251 1
5.3%
135925 1
5.3%
93346 1
5.3%
85864 1
5.3%
73304 1
5.3%
71094 1
5.3%
66439 1
5.3%
41790 1
5.3%
41108 1
5.3%
39127 1
5.3%

physicalDamageDealtToChampions
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10246.211
Minimum1409
Maximum24903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:42.361615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1409
5-th percentile3819.2
Q16921
median11097
Q312576
95-th percentile17305.2
Maximum24903
Range23494
Interquartile range (IQR)5655

Descriptive statistics

Standard deviation5239.6469
Coefficient of variation (CV)0.51137412
Kurtosis2.2956299
Mean10246.211
Median Absolute Deviation (MAD)3047
Skewness0.98008414
Sum194678
Variance27453900
MonotonicityNot monotonic
2025-03-06T10:38:42.417135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
16461 1
 
5.3%
6648 1
 
5.3%
5224 1
 
5.3%
11655 1
 
5.3%
12804 1
 
5.3%
7194 1
 
5.3%
1409 1
 
5.3%
8398 1
 
5.3%
8516 1
 
5.3%
11211 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
1409 1
5.3%
4087 1
5.3%
5224 1
5.3%
5709 1
5.3%
6648 1
5.3%
7194 1
5.3%
7596 1
5.3%
8398 1
5.3%
8516 1
5.3%
11097 1
5.3%
ValueCountFrequency (%)
24903 1
5.3%
16461 1
5.3%
14144 1
5.3%
13628 1
5.3%
12804 1
5.3%
12348 1
5.3%
11655 1
5.3%
11646 1
5.3%
11211 1
5.3%
11097 1
5.3%

physicalDamageTaken
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20929.789
Minimum2843
Maximum53056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:42.469133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2843
5-th percentile5795.9
Q111971.5
median16557
Q326641
95-th percentile48834.1
Maximum53056
Range50213
Interquartile range (IQR)14669.5

Descriptive statistics

Standard deviation14433.988
Coefficient of variation (CV)0.68963845
Kurtosis0.15043735
Mean20929.789
Median Absolute Deviation (MAD)5494
Skewness1.0401098
Sum397666
Variance2.0834 × 108
MonotonicityNot monotonic
2025-03-06T10:38:42.522652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
15550 1
 
5.3%
18099 1
 
5.3%
6124 1
 
5.3%
34259 1
 
5.3%
12880 1
 
5.3%
8569 1
 
5.3%
2843 1
 
5.3%
11063 1
 
5.3%
6418 1
 
5.3%
18074 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
2843 1
5.3%
6124 1
5.3%
6418 1
5.3%
8569 1
5.3%
11063 1
5.3%
12880 1
5.3%
15550 1
5.3%
15773 1
5.3%
16413 1
5.3%
16557 1
5.3%
ValueCountFrequency (%)
53056 1
5.3%
48365 1
5.3%
39379 1
5.3%
36865 1
5.3%
34259 1
5.3%
19023 1
5.3%
18356 1
5.3%
18099 1
5.3%
18074 1
5.3%
16557 1
5.3%

placement
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:42.581658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.617172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

quadraKills
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:42.656172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.793689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

role
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
SOLO
12 
SUPPORT
DUO
NONE

Length

Max length7
Median length4
Mean length4.3684211
Min length3

Characters and Unicode

Total characters83
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOLO
2nd rowDUO
3rd rowSUPPORT
4th rowSOLO
5th rowNONE

Common Values

ValueCountFrequency (%)
SOLO 12
63.2%
SUPPORT 3
 
15.8%
DUO 2
 
10.5%
NONE 2
 
10.5%

Length

2025-03-06T10:38:42.838200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.885207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
solo 12
63.2%
support 3
 
15.8%
duo 2
 
10.5%
none 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
O 31
37.3%
S 15
18.1%
L 12
 
14.5%
P 6
 
7.2%
U 5
 
6.0%
N 4
 
4.8%
R 3
 
3.6%
T 3
 
3.6%
D 2
 
2.4%
E 2
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 31
37.3%
S 15
18.1%
L 12
 
14.5%
P 6
 
7.2%
U 5
 
6.0%
N 4
 
4.8%
R 3
 
3.6%
T 3
 
3.6%
D 2
 
2.4%
E 2
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 31
37.3%
S 15
18.1%
L 12
 
14.5%
P 6
 
7.2%
U 5
 
6.0%
N 4
 
4.8%
R 3
 
3.6%
T 3
 
3.6%
D 2
 
2.4%
E 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 31
37.3%
S 15
18.1%
L 12
 
14.5%
P 6
 
7.2%
U 5
 
6.0%
N 4
 
4.8%
R 3
 
3.6%
T 3
 
3.6%
D 2
 
2.4%
E 2
 
2.4%

sightWardsBoughtInGame
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:42.939308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:42.973308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

spell1Casts
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.10526
Minimum24
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.006357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile26.7
Q167
median104
Q3128
95-th percentile171.2
Maximum173
Range149
Interquartile range (IQR)61

Descriptive statistics

Standard deviation46.191503
Coefficient of variation (CV)0.46142932
Kurtosis-0.84352124
Mean100.10526
Median Absolute Deviation (MAD)36
Skewness0.069499829
Sum1902
Variance2133.655
MonotonicityNot monotonic
2025-03-06T10:38:43.062359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
112 1
 
5.3%
62 1
 
5.3%
46 1
 
5.3%
99 1
 
5.3%
66 1
 
5.3%
71 1
 
5.3%
27 1
 
5.3%
86 1
 
5.3%
105 1
 
5.3%
117 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
24 1
5.3%
27 1
5.3%
46 1
5.3%
62 1
5.3%
66 1
5.3%
68 1
5.3%
71 1
5.3%
86 1
5.3%
99 1
5.3%
104 1
5.3%
ValueCountFrequency (%)
173 1
5.3%
171 1
5.3%
169 1
5.3%
149 1
5.3%
139 1
5.3%
117 1
5.3%
114 1
5.3%
112 1
5.3%
105 1
5.3%
104 1
5.3%

spell2Casts
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.157895
Minimum4
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.114869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10.3
Q116
median23
Q332.5
95-th percentile45
Maximum63
Range59
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation13.728572
Coefficient of variation (CV)0.54569637
Kurtosis1.9036976
Mean25.157895
Median Absolute Deviation (MAD)9
Skewness1.098963
Sum478
Variance188.47368
MonotonicityNot monotonic
2025-03-06T10:38:43.160873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
32 2
 
10.5%
16 2
 
10.5%
33 2
 
10.5%
12 1
 
5.3%
19 1
 
5.3%
4 1
 
5.3%
14 1
 
5.3%
24 1
 
5.3%
11 1
 
5.3%
17 1
 
5.3%
Other values (6) 6
31.6%
ValueCountFrequency (%)
4 1
5.3%
11 1
5.3%
12 1
5.3%
14 1
5.3%
16 2
10.5%
17 1
5.3%
19 1
5.3%
22 1
5.3%
23 1
5.3%
24 1
5.3%
ValueCountFrequency (%)
63 1
5.3%
43 1
5.3%
39 1
5.3%
33 2
10.5%
32 2
10.5%
25 1
5.3%
24 1
5.3%
23 1
5.3%
22 1
5.3%
19 1
5.3%

spell3Casts
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.263158
Minimum9
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.211392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15.3
Q130.5
median45
Q372
95-th percentile105.3
Maximum117
Range108
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation29.862355
Coefficient of variation (CV)0.58253054
Kurtosis-0.15366789
Mean51.263158
Median Absolute Deviation (MAD)18
Skewness0.73499169
Sum974
Variance891.76023
MonotonicityNot monotonic
2025-03-06T10:38:43.266392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
48 1
 
5.3%
28 1
 
5.3%
16 1
 
5.3%
49 1
 
5.3%
38 1
 
5.3%
30 1
 
5.3%
9 1
 
5.3%
31 1
 
5.3%
37 1
 
5.3%
34 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
9 1
5.3%
16 1
5.3%
20 1
5.3%
28 1
5.3%
30 1
5.3%
31 1
5.3%
34 1
5.3%
37 1
5.3%
38 1
5.3%
45 1
5.3%
ValueCountFrequency (%)
117 1
5.3%
104 1
5.3%
81 1
5.3%
80 1
5.3%
79 1
5.3%
65 1
5.3%
63 1
5.3%
49 1
5.3%
48 1
5.3%
45 1
5.3%

spell4Casts
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1052632
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.316912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.9
Q14.5
median7
Q37.5
95-th percentile9.3
Maximum12
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4471012
Coefficient of variation (CV)0.40081829
Kurtosis0.39758012
Mean6.1052632
Median Absolute Deviation (MAD)2
Skewness0.42345928
Sum116
Variance5.9883041
MonotonicityNot monotonic
2025-03-06T10:38:43.359912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 5
26.3%
5 4
21.1%
8 3
15.8%
3 2
 
10.5%
4 2
 
10.5%
2 1
 
5.3%
9 1
 
5.3%
12 1
 
5.3%
ValueCountFrequency (%)
2 1
 
5.3%
3 2
 
10.5%
4 2
 
10.5%
5 4
21.1%
7 5
26.3%
8 3
15.8%
9 1
 
5.3%
12 1
 
5.3%
ValueCountFrequency (%)
12 1
 
5.3%
9 1
 
5.3%
8 3
15.8%
7 5
26.3%
5 4
21.1%
4 2
 
10.5%
3 2
 
10.5%
2 1
 
5.3%

subteamPlacement
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:43.413420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:43.446420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

summoner1Casts
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
3
2
4
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

Length

2025-03-06T10:38:43.484424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:43.528934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7
36.8%
2 6
31.6%
4 4
21.1%
1 2
 
10.5%

summoner1Id
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
12
19 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters38
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 19
100.0%

Length

2025-03-06T10:38:43.581942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:43.616455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12 19
100.0%

Most occurring characters

ValueCountFrequency (%)
1 19
50.0%
2 19
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19
50.0%
2 19
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19
50.0%
2 19
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19
50.0%
2 19
50.0%

summoner2Casts
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1052632
Minimum0
Maximum6
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.646457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q12
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6294081
Coefficient of variation (CV)0.52472464
Kurtosis-0.32792343
Mean3.1052632
Median Absolute Deviation (MAD)1
Skewness0.070134861
Sum59
Variance2.6549708
MonotonicityNot monotonic
2025-03-06T10:38:43.690461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5
26.3%
2 4
21.1%
3 4
21.1%
1 2
 
10.5%
6 2
 
10.5%
0 1
 
5.3%
5 1
 
5.3%
ValueCountFrequency (%)
0 1
 
5.3%
1 2
 
10.5%
2 4
21.1%
3 4
21.1%
4 5
26.3%
5 1
 
5.3%
6 2
 
10.5%
ValueCountFrequency (%)
6 2
 
10.5%
5 1
 
5.3%
4 5
26.3%
3 4
21.1%
2 4
21.1%
1 2
 
10.5%
0 1
 
5.3%

summoner2Id
Categorical

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
4
14 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

Length

2025-03-06T10:38:43.747977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:43.784985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

Most occurring characters

ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 14
73.7%
6 5
 
26.3%

teamEarlySurrendered
Boolean

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
ValueCountFrequency (%)
False 19
100.0%
2025-03-06T10:38:43.806495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

teamPosition
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
TOP
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOP
2nd rowTOP
3rd rowTOP
4th rowTOP
5th rowTOP

Common Values

ValueCountFrequency (%)
TOP 19
100.0%

Length

2025-03-06T10:38:43.847495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:43.880495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
top 19
100.0%

Most occurring characters

ValueCountFrequency (%)
T 19
33.3%
O 19
33.3%
P 19
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 19
33.3%
O 19
33.3%
P 19
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 19
33.3%
O 19
33.3%
P 19
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 19
33.3%
O 19
33.3%
P 19
33.3%

timeCCingOthers
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.789474
Minimum6
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:43.913519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8.7
Q118.5
median23
Q330.5
95-th percentile54
Maximum63
Range57
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.339452
Coefficient of variation (CV)0.53526442
Kurtosis1.3370773
Mean26.789474
Median Absolute Deviation (MAD)6
Skewness1.118093
Sum509
Variance205.61988
MonotonicityNot monotonic
2025-03-06T10:38:43.965518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
23 2
 
10.5%
17 2
 
10.5%
63 1
 
5.3%
46 1
 
5.3%
22 1
 
5.3%
31 1
 
5.3%
6 1
 
5.3%
21 1
 
5.3%
53 1
 
5.3%
30 1
 
5.3%
Other values (7) 7
36.8%
ValueCountFrequency (%)
6 1
5.3%
9 1
5.3%
13 1
5.3%
17 2
10.5%
20 1
5.3%
21 1
5.3%
22 1
5.3%
23 2
10.5%
26 1
5.3%
27 1
5.3%
ValueCountFrequency (%)
63 1
5.3%
53 1
5.3%
46 1
5.3%
33 1
5.3%
31 1
5.3%
30 1
5.3%
29 1
5.3%
27 1
5.3%
26 1
5.3%
23 2
10.5%

timePlayed
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1694.2632
Minimum839
Maximum2547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.015036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum839
5-th percentile953.3
Q11546
median1706
Q31897
95-th percentile2215.8
Maximum2547
Range1708
Interquartile range (IQR)351

Descriptive statistics

Standard deviation401.76802
Coefficient of variation (CV)0.23713436
Kurtosis0.92412328
Mean1694.2632
Median Absolute Deviation (MAD)180
Skewness-0.33435014
Sum32191
Variance161417.54
MonotonicityNot monotonic
2025-03-06T10:38:44.067036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1897 2
 
10.5%
1619 1
 
5.3%
966 1
 
5.3%
2547 1
 
5.3%
1565 1
 
5.3%
1163 1
 
5.3%
839 1
 
5.3%
1780 1
 
5.3%
1526 1
 
5.3%
1683 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
839 1
5.3%
966 1
5.3%
1163 1
5.3%
1526 1
5.3%
1527 1
5.3%
1565 1
5.3%
1619 1
5.3%
1683 1
5.3%
1694 1
5.3%
1706 1
5.3%
ValueCountFrequency (%)
2547 1
5.3%
2179 1
5.3%
2063 1
5.3%
1971 1
5.3%
1897 2
10.5%
1816 1
5.3%
1780 1
5.3%
1753 1
5.3%
1706 1
5.3%
1694 1
5.3%

totalAllyJungleMinionsKilled
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:44.124547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:44.156547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

totalDamageDealt
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91437.895
Minimum11558
Maximum191032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.189060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11558
5-th percentile26498.9
Q159221
median100468
Q3117867
95-th percentile182987.8
Maximum191032
Range179474
Interquartile range (IQR)58646

Descriptive statistics

Standard deviation49541.747
Coefficient of variation (CV)0.54180761
Kurtosis-0.31166461
Mean91437.895
Median Absolute Deviation (MAD)40756
Skewness0.45774583
Sum1737320
Variance2.4543847 × 109
MonotonicityNot monotonic
2025-03-06T10:38:44.241567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
69187 1
 
5.3%
44868 1
 
5.3%
28159 1
 
5.3%
118473 1
 
5.3%
59259 1
 
5.3%
39644 1
 
5.3%
11558 1
 
5.3%
67145 1
 
5.3%
59183 1
 
5.3%
100904 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
11558 1
5.3%
28159 1
5.3%
39644 1
5.3%
44868 1
5.3%
59183 1
5.3%
59259 1
5.3%
59712 1
5.3%
67145 1
5.3%
69187 1
5.3%
100468 1
5.3%
ValueCountFrequency (%)
191032 1
5.3%
182094 1
5.3%
151342 1
5.3%
118473 1
5.3%
118335 1
5.3%
117399 1
5.3%
116789 1
5.3%
101769 1
5.3%
100904 1
5.3%
100468 1
5.3%

totalDamageDealtToChampions
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20228.684
Minimum2643
Maximum48324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.298084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2643
5-th percentile8798.1
Q113768
median21686
Q323872.5
95-th percentile32709.9
Maximum48324
Range45681
Interquartile range (IQR)10104.5

Descriptive statistics

Standard deviation10099.386
Coefficient of variation (CV)0.49926065
Kurtosis2.2140083
Mean20228.684
Median Absolute Deviation (MAD)7126
Skewness0.96257358
Sum384345
Variance1.019976 × 108
MonotonicityNot monotonic
2025-03-06T10:38:44.350085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
28623 1
 
5.3%
10551 1
 
5.3%
9482 1
 
5.3%
21686 1
 
5.3%
23101 1
 
5.3%
14116 1
 
5.3%
2643 1
 
5.3%
13420 1
 
5.3%
14979 1
 
5.3%
22987 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
2643 1
5.3%
9482 1
5.3%
10551 1
5.3%
12376 1
5.3%
13420 1
5.3%
14116 1
5.3%
14560 1
5.3%
14979 1
5.3%
17725 1
5.3%
21686 1
5.3%
ValueCountFrequency (%)
48324 1
5.3%
30975 1
5.3%
29338 1
5.3%
28623 1
5.3%
24644 1
5.3%
23101 1
5.3%
23099 1
5.3%
22987 1
5.3%
21716 1
5.3%
21686 1
5.3%

totalDamageShieldedOnTeammates
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1154.0526
Minimum0
Maximum3982
Zeros8
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.399604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median663
Q32271.5
95-th percentile3256.6
Maximum3982
Range3982
Interquartile range (IQR)2271.5

Descriptive statistics

Standard deviation1312.381
Coefficient of variation (CV)1.1371933
Kurtosis-0.76976748
Mean1154.0526
Median Absolute Deviation (MAD)663
Skewness0.73657669
Sum21927
Variance1722343.8
MonotonicityNot monotonic
2025-03-06T10:38:44.449116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 8
42.1%
2489 1
 
5.3%
689 1
 
5.3%
1620 1
 
5.3%
3982 1
 
5.3%
2248 1
 
5.3%
346 1
 
5.3%
663 1
 
5.3%
2295 1
 
5.3%
3176 1
 
5.3%
Other values (2) 2
 
10.5%
ValueCountFrequency (%)
0 8
42.1%
346 1
 
5.3%
663 1
 
5.3%
689 1
 
5.3%
1620 1
 
5.3%
1748 1
 
5.3%
2248 1
 
5.3%
2295 1
 
5.3%
2489 1
 
5.3%
2671 1
 
5.3%
ValueCountFrequency (%)
3982 1
5.3%
3176 1
5.3%
2671 1
5.3%
2489 1
5.3%
2295 1
5.3%
2248 1
5.3%
1748 1
5.3%
1620 1
5.3%
689 1
5.3%
663 1
5.3%

totalDamageTaken
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34850.895
Minimum2843
Maximum83169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.502636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2843
5-th percentile7801.1
Q122729
median30198
Q343865.5
95-th percentile72546.3
Maximum83169
Range80326
Interquartile range (IQR)21136.5

Descriptive statistics

Standard deviation19894.433
Coefficient of variation (CV)0.57084426
Kurtosis0.95652202
Mean34850.895
Median Absolute Deviation (MAD)11631
Skewness0.80653832
Sum662167
Variance3.9578848 × 108
MonotonicityNot monotonic
2025-03-06T10:38:44.560637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
30198 1
 
5.3%
29696 1
 
5.3%
8352 1
 
5.3%
50385 1
 
5.3%
26126 1
 
5.3%
15632 1
 
5.3%
2843 1
 
5.3%
23380 1
 
5.3%
18567 1
 
5.3%
38417 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
2843 1
5.3%
8352 1
5.3%
15632 1
5.3%
18567 1
5.3%
22078 1
5.3%
23380 1
5.3%
26126 1
5.3%
29272 1
5.3%
29696 1
5.3%
30198 1
5.3%
ValueCountFrequency (%)
83169 1
5.3%
71366 1
5.3%
50385 1
5.3%
48965 1
5.3%
45074 1
5.3%
42657 1
5.3%
39773 1
5.3%
38417 1
5.3%
36217 1
5.3%
30198 1
5.3%

totalEnemyJungleMinionsKilled
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:44.624146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:44.667146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

totalHeal
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6216.2105
Minimum156
Maximum20957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.700660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile451.2
Q11964.5
median2861
Q310341.5
95-th percentile20124.5
Maximum20957
Range20801
Interquartile range (IQR)8377

Descriptive statistics

Standard deviation6653.0388
Coefficient of variation (CV)1.0702724
Kurtosis0.3546006
Mean6216.2105
Median Absolute Deviation (MAD)1068
Skewness1.2823936
Sum118108
Variance44262925
MonotonicityNot monotonic
2025-03-06T10:38:44.752657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3045 1
 
5.3%
2097 1
 
5.3%
1131 1
 
5.3%
2861 1
 
5.3%
1832 1
 
5.3%
2594 1
 
5.3%
156 1
 
5.3%
2834 1
 
5.3%
1793 1
 
5.3%
3247 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
156 1
5.3%
484 1
5.3%
1131 1
5.3%
1793 1
5.3%
1832 1
5.3%
2097 1
5.3%
2594 1
5.3%
2834 1
5.3%
2848 1
5.3%
2861 1
5.3%
ValueCountFrequency (%)
20957 1
5.3%
20032 1
5.3%
15824 1
5.3%
12182 1
5.3%
11357 1
5.3%
9326 1
5.3%
3508 1
5.3%
3247 1
5.3%
3045 1
5.3%
2861 1
5.3%

totalHealsOnTeammates
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:44.809170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:44.841169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

totalMinionsKilled
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.68421
Minimum55
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:44.877171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile88.3
Q1109.5
median140
Q3187
95-th percentile200.8
Maximum208
Range153
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation45.667947
Coefficient of variation (CV)0.31347218
Kurtosis-1.0465883
Mean145.68421
Median Absolute Deviation (MAD)41
Skewness-0.28151631
Sum2768
Variance2085.5614
MonotonicityNot monotonic
2025-03-06T10:38:44.933696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
95 1
 
5.3%
99 1
 
5.3%
92 1
 
5.3%
128 1
 
5.3%
126 1
 
5.3%
93 1
 
5.3%
55 1
 
5.3%
175 1
 
5.3%
140 1
 
5.3%
176 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
55 1
5.3%
92 1
5.3%
93 1
5.3%
95 1
5.3%
99 1
5.3%
120 1
5.3%
126 1
5.3%
128 1
5.3%
138 1
5.3%
140 1
5.3%
ValueCountFrequency (%)
208 1
5.3%
200 1
5.3%
199 1
5.3%
195 1
5.3%
193 1
5.3%
181 1
5.3%
176 1
5.3%
175 1
5.3%
155 1
5.3%
140 1
5.3%

totalTimeCCDealt
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.94737
Minimum88
Maximum548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.086210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile142.9
Q1206
median248
Q3277
95-th percentile412.1
Maximum548
Range460
Interquartile range (IQR)71

Descriptive statistics

Standard deviation97.180516
Coefficient of variation (CV)0.38571753
Kurtosis4.328259
Mean251.94737
Median Absolute Deviation (MAD)39
Skewness1.5088247
Sum4787
Variance9444.0526
MonotonicityNot monotonic
2025-03-06T10:38:45.138724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
203 2
 
10.5%
280 1
 
5.3%
153 1
 
5.3%
149 1
 
5.3%
209 1
 
5.3%
243 1
 
5.3%
248 1
 
5.3%
88 1
 
5.3%
222 1
 
5.3%
297 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
88 1
5.3%
149 1
5.3%
153 1
5.3%
203 2
10.5%
209 1
5.3%
212 1
5.3%
222 1
5.3%
243 1
5.3%
248 1
5.3%
250 1
5.3%
ValueCountFrequency (%)
548 1
5.3%
397 1
5.3%
297 1
5.3%
288 1
5.3%
280 1
5.3%
274 1
5.3%
265 1
5.3%
258 1
5.3%
250 1
5.3%
248 1
5.3%

totalTimeSpentDead
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.63158
Minimum0
Maximum317
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.186723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144
median142
Q3213
95-th percentile254
Maximum317
Range317
Interquartile range (IQR)169

Descriptive statistics

Standard deviation93.433405
Coefficient of variation (CV)0.69918657
Kurtosis-0.999813
Mean133.63158
Median Absolute Deviation (MAD)80
Skewness0.15694152
Sum2539
Variance8729.8012
MonotonicityNot monotonic
2025-03-06T10:38:45.241233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
10.5%
132 1
 
5.3%
93 1
 
5.3%
317 1
 
5.3%
142 1
 
5.3%
28 1
 
5.3%
49 1
 
5.3%
38 1
 
5.3%
143 1
 
5.3%
39 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0 2
10.5%
28 1
5.3%
38 1
5.3%
39 1
5.3%
49 1
5.3%
86 1
5.3%
93 1
5.3%
132 1
5.3%
142 1
5.3%
143 1
5.3%
ValueCountFrequency (%)
317 1
5.3%
247 1
5.3%
228 1
5.3%
222 1
5.3%
218 1
5.3%
208 1
5.3%
204 1
5.3%
145 1
5.3%
143 1
5.3%
142 1
5.3%

totalUnitsHealed
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19
100.0%

Length

2025-03-06T10:38:45.300745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:45.333744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 19
100.0%

Most occurring characters

ValueCountFrequency (%)
1 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19
100.0%

tripleKills
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
18 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Length

2025-03-06T10:38:45.374745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:45.409273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18
94.7%
1 1
 
5.3%

trueDamageDealt
Real number (ℝ)

High correlation  Unique 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7865.0526
Minimum82
Maximum33176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.443271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile178.3
Q13369
median5035
Q39795.5
95-th percentile22670.3
Maximum33176
Range33094
Interquartile range (IQR)6426.5

Descriptive statistics

Standard deviation8351.1529
Coefficient of variation (CV)1.0618051
Kurtosis3.8743893
Mean7865.0526
Median Absolute Deviation (MAD)3785
Skewness1.8739365
Sum149436
Variance69741754
MonotonicityNot monotonic
2025-03-06T10:38:45.496784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10771 1
 
5.3%
3399 1
 
5.3%
875 1
 
5.3%
33176 1
 
5.3%
11456 1
 
5.3%
5035 1
 
5.3%
82 1
 
5.3%
8820 1
 
5.3%
3643 1
 
5.3%
17153 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
82 1
5.3%
189 1
5.3%
474 1
5.3%
875 1
5.3%
3339 1
5.3%
3399 1
5.3%
3643 1
5.3%
4131 1
5.3%
4564 1
5.3%
5035 1
5.3%
ValueCountFrequency (%)
33176 1
5.3%
21503 1
5.3%
17153 1
5.3%
11456 1
5.3%
10771 1
5.3%
8820 1
5.3%
8423 1
5.3%
6561 1
5.3%
5842 1
5.3%
5035 1
5.3%

trueDamageDealtToChampions
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.78947
Minimum0
Maximum1862
Zeros3
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.546793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1106
median389
Q3958.5
95-th percentile1337.3
Maximum1862
Range1862
Interquartile range (IQR)852.5

Descriptive statistics

Standard deviation530.3116
Coefficient of variation (CV)0.92745954
Kurtosis0.087562472
Mean571.78947
Median Absolute Deviation (MAD)389
Skewness0.7939791
Sum10864
Variance281230.4
MonotonicityNot monotonic
2025-03-06T10:38:45.600386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3
15.8%
1862 1
 
5.3%
358 1
 
5.3%
1098 1
 
5.3%
389 1
 
5.3%
932 1
 
5.3%
82 1
 
5.3%
492 1
 
5.3%
1051 1
 
5.3%
866 1
 
5.3%
Other values (7) 7
36.8%
ValueCountFrequency (%)
0 3
15.8%
28 1
 
5.3%
82 1
 
5.3%
130 1
 
5.3%
214 1
 
5.3%
352 1
 
5.3%
358 1
 
5.3%
389 1
 
5.3%
492 1
 
5.3%
746 1
 
5.3%
ValueCountFrequency (%)
1862 1
5.3%
1279 1
5.3%
1098 1
5.3%
1051 1
5.3%
985 1
5.3%
932 1
5.3%
866 1
5.3%
746 1
5.3%
492 1
5.3%
389 1
5.3%

trueDamageTaken
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2321.5789
Minimum0
Maximum10728
Zeros1
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.653384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119.7
Q1724.5
median1182
Q33529.5
95-th percentile6988.5
Maximum10728
Range10728
Interquartile range (IQR)2805

Descriptive statistics

Standard deviation2782.3567
Coefficient of variation (CV)1.198476
Kurtosis3.5978697
Mean2321.5789
Median Absolute Deviation (MAD)916
Skewness1.8743241
Sum44110
Variance7741508.8
MonotonicityNot monotonic
2025-03-06T10:38:45.711908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1182 1
 
5.3%
964 1
 
5.3%
485 1
 
5.3%
6573 1
 
5.3%
1297 1
 
5.3%
133 1
 
5.3%
0 1
 
5.3%
266 1
 
5.3%
155 1
 
5.3%
4867 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0 1
5.3%
133 1
5.3%
155 1
5.3%
266 1
5.3%
485 1
5.3%
964 1
5.3%
1013 1
5.3%
1024 1
5.3%
1162 1
5.3%
1182 1
5.3%
ValueCountFrequency (%)
10728 1
5.3%
6573 1
5.3%
4867 1
5.3%
4639 1
5.3%
4593 1
5.3%
2466 1
5.3%
1341 1
5.3%
1297 1
5.3%
1222 1
5.3%
1182 1
5.3%

turretKills
Categorical

High correlation 

Distinct5
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
10 
1
3
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

Length

2025-03-06T10:38:45.768908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:45.813936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10
52.6%
1 5
26.3%
3 2
 
10.5%
2 1
 
5.3%
4 1
 
5.3%

turretTakedowns
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8421053
Minimum0
Maximum8
Zeros8
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.857937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.5
95-th percentile5.3
Maximum8
Range8
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3395906
Coefficient of variation (CV)1.2700635
Kurtosis1.0274203
Mean1.8421053
Median Absolute Deviation (MAD)1
Skewness1.2884807
Sum35
Variance5.4736842
MonotonicityNot monotonic
2025-03-06T10:38:45.906456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 8
42.1%
1 4
21.1%
4 2
 
10.5%
5 2
 
10.5%
2 1
 
5.3%
8 1
 
5.3%
3 1
 
5.3%
ValueCountFrequency (%)
0 8
42.1%
1 4
21.1%
2 1
 
5.3%
3 1
 
5.3%
4 2
 
10.5%
5 2
 
10.5%
8 1
 
5.3%
ValueCountFrequency (%)
8 1
 
5.3%
5 2
 
10.5%
4 2
 
10.5%
3 1
 
5.3%
2 1
 
5.3%
1 4
21.1%
0 8
42.1%

turretsLost
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2105263
Minimum0
Maximum12
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:45.953456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q39
95-th percentile11.1
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8090382
Coefficient of variation (CV)0.61331971
Kurtosis-1.2912811
Mean6.2105263
Median Absolute Deviation (MAD)3
Skewness-0.26865844
Sum118
Variance14.508772
MonotonicityNot monotonic
2025-03-06T10:38:46.007977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3
15.8%
3 3
15.8%
2 2
10.5%
7 2
10.5%
8 2
10.5%
0 2
10.5%
10 2
10.5%
5 1
 
5.3%
12 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
0 2
10.5%
2 2
10.5%
3 3
15.8%
5 1
 
5.3%
7 2
10.5%
8 2
10.5%
9 3
15.8%
10 2
10.5%
11 1
 
5.3%
12 1
 
5.3%
ValueCountFrequency (%)
12 1
 
5.3%
11 1
 
5.3%
10 2
10.5%
9 3
15.8%
8 2
10.5%
7 2
10.5%
5 1
 
5.3%
3 3
15.8%
2 2
10.5%
0 2
10.5%

unrealKills
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:46.065979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:46.098487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

visionClearedPings
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:46.139489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:46.172489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

visionScore
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.421053
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:46.204003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.5
Q112.5
median15
Q317
95-th percentile22.3
Maximum25
Range24
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.6106624
Coefficient of variation (CV)0.38906053
Kurtosis0.8863093
Mean14.421053
Median Absolute Deviation (MAD)2
Skewness-0.51086274
Sum274
Variance31.479532
MonotonicityNot monotonic
2025-03-06T10:38:46.252003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16 3
15.8%
13 3
15.8%
17 2
10.5%
15 2
10.5%
19 1
 
5.3%
25 1
 
5.3%
8 1
 
5.3%
1 1
 
5.3%
6 1
 
5.3%
12 1
 
5.3%
Other values (3) 3
15.8%
ValueCountFrequency (%)
1 1
 
5.3%
6 1
 
5.3%
8 1
 
5.3%
10 1
 
5.3%
12 1
 
5.3%
13 3
15.8%
15 2
10.5%
16 3
15.8%
17 2
10.5%
19 1
 
5.3%
ValueCountFrequency (%)
25 1
 
5.3%
22 1
 
5.3%
20 1
 
5.3%
19 1
 
5.3%
17 2
10.5%
16 3
15.8%
15 2
10.5%
13 3
15.8%
12 1
 
5.3%
10 1
 
5.3%

visionWardsBoughtInGame
Categorical

Constant 

Distinct1
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
100.0%

Length

2025-03-06T10:38:46.305516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:46.339516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 19
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
100.0%

wardsKilled
Categorical

High correlation 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
2
1
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st row4
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

Length

2025-03-06T10:38:46.377516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-06T10:38:46.418028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8
42.1%
2 5
26.3%
1 5
26.3%
4 1
 
5.3%

wardsPlaced
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1578947
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2025-03-06T10:38:46.458028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q17
median9
Q39
95-th percentile12.3
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0048693
Coefficient of variation (CV)0.36833882
Kurtosis1.6831281
Mean8.1578947
Median Absolute Deviation (MAD)2
Skewness-0.15575907
Sum155
Variance9.0292398
MonotonicityNot monotonic
2025-03-06T10:38:46.504064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 6
31.6%
7 5
26.3%
10 1
 
5.3%
5 1
 
5.3%
15 1
 
5.3%
4 1
 
5.3%
1 1
 
5.3%
8 1
 
5.3%
12 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
1 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%
7 5
26.3%
8 1
 
5.3%
9 6
31.6%
10 1
 
5.3%
11 1
 
5.3%
12 1
 
5.3%
15 1
 
5.3%
ValueCountFrequency (%)
15 1
 
5.3%
12 1
 
5.3%
11 1
 
5.3%
10 1
 
5.3%
9 6
31.6%
8 1
 
5.3%
7 5
26.3%
5 1
 
5.3%
4 1
 
5.3%
1 1
 
5.3%

win
Boolean

High correlation 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
11 
True
ValueCountFrequency (%)
False 11
57.9%
True 8
42.1%
2025-03-06T10:38:46.544063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-03-06T10:38:35.665108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:06.158662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:08.114580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:10.252859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:12.267700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:14.497963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:16.722615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:18.923279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:20.907228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:23.053907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:25.017047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:27.043134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:29.069861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:31.073788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:33.088097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:35.359282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:37.328860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:39.341991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:41.458217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:43.626410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:45.661349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:47.689705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:49.884789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:51.878058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:54.019708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:56.010553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:58.069231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-06T10:38:02.331605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-06T10:37:59.974510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:02.136610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:04.447850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:06.584139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:08.938903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:11.550000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:14.288623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:17.033684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:19.341621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:21.783199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:24.179128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:26.799565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:28.992785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:31.282136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:33.437616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:35.487084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:37.640221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:07.984069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:10.027061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:12.041373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:14.350693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:16.572413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:18.772156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:20.773530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:22.908847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:24.885628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:26.908041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:28.933423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:30.944134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:32.958166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:35.202819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:37.201189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:39.209625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:41.319217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:43.479863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:45.524908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:47.553869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:49.735231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:51.744179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:53.880145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:55.877599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:57.930746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:00.024679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:02.189776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:04.507718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:06.634771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:09.002616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:11.613513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:14.352623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:17.195735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:19.389135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:21.834217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:24.364660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:26.857560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:29.042789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:31.333344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:33.485623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:35.534592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:37.684224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:08.027600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:10.164310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:12.084069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:14.401589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:16.624293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:18.822337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:20.818192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:22.960149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:24.931459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:26.953807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:28.982797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:30.988157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:33.001478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:35.257771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:37.241301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:39.251199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:41.361356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:43.528655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:45.569675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:47.601616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:49.788739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:51.791487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:53.925437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:55.921047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:57.977038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:00.072466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:02.238222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:04.567820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:06.680115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:09.059548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:11.667513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:14.415143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:17.244735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:19.435644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:21.883220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:24.413174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:26.909073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:29.088303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:31.383349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:33.529129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:35.579592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:37.728731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:08.071513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:10.208627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:12.225174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:14.452426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:16.671401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:18.872965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:20.857862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:23.004817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:24.973008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:26.999615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:29.024103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:31.032108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:33.042188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:35.306358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:37.287832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:39.297145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:41.411259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:43.578894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:45.616811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:47.645057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:49.836550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:51.836299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:53.972468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:55.966919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:37:58.022861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:00.120832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:02.283751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:04.618553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:06.725215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:09.117252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:11.854100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:14.475146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:17.297769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:19.480656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:22.027245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:24.468174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:26.959076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:29.133812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:31.435860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:33.574131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-06T10:38:35.622108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-06T10:38:46.644577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
assistsbountyLevelchampLevelchampionIdchampionNamedamageDealtToBuildingsdamageDealtToObjectivesdamageDealtToTurretsdamageSelfMitigateddeathsdoubleKillseligibleForProgressionfirstTowerAssistgameEndedInSurrendergoldEarnedgoldSpentinhibitorKillsinhibitorTakedownsinhibitorsLostitemsPurchasedkillingSpreeskillslanelargestKillingSpreelargestMultiKilllongestTimeSpentLivingmagicDamageDealtmagicDamageDealtToChampionsmagicDamageTakenneutralMinionsKillednexusKillsnexusLostnexusTakedownsobjectivesStolenphysicalDamageDealtphysicalDamageDealtToChampionsphysicalDamageTakenrolespell1Castsspell2Castsspell3Castsspell4Castssummoner1Castssummoner2Castssummoner2IdtimeCCingOtherstimePlayedtotalDamageDealttotalDamageDealtToChampionstotalDamageShieldedOnTeammatestotalDamageTakentotalHealtotalMinionsKilledtotalTimeCCDealttotalTimeSpentDeadtripleKillstrueDamageDealttrueDamageDealtToChampionstrueDamageTakenturretKillsturretTakedownsturretsLostvisionScorewardsKilledwardsPlacedwin
assists1.0000.2980.3870.0000.0000.0150.1680.0150.465-0.0340.0000.0000.0000.3990.3030.2140.0000.0000.0000.1020.0000.2980.0000.3220.0000.4860.1800.1730.2830.2970.0000.3260.1910.503-0.1950.519-0.0040.0000.1800.0790.1250.1110.0000.0440.1840.6560.335-0.0080.3290.7120.1440.138-0.1850.142-0.0340.0000.4820.3930.1290.0000.270-0.0280.5500.5040.4500.326
bountyLevel0.2981.0000.0310.0000.000-0.063-0.224-0.0630.1110.1680.1850.0000.1400.2240.3000.3560.4970.6710.000-0.1180.4211.0000.0000.7830.5430.197-0.254-0.0510.0800.4130.0000.2020.3900.0000.0620.494-0.1270.000-0.1310.1070.0760.2730.0000.1950.3440.593-0.114-0.0210.2380.189-0.087-0.103-0.109-0.0680.1160.8740.2350.3590.1720.2840.225-0.1530.0200.0000.1710.202
champLevel0.3870.0311.0000.0000.0000.4950.3800.4950.7360.6190.4650.8040.8040.0000.8720.8340.8040.6450.0000.7880.0000.0310.316-0.1850.5920.1740.7970.7060.4950.1360.0000.1930.3620.3920.6780.6180.7340.3070.7580.6610.7960.5200.4710.7200.0000.0980.8630.7950.705-0.0690.8320.8150.5980.7700.6540.8040.3640.0120.6390.3250.5250.1760.7070.0000.6790.193
championId0.0000.0000.0001.0001.0000.0000.0000.0000.3650.4920.1260.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0000.0000.0000.0000.6170.2110.4600.0000.0000.0000.0000.0000.6610.0000.0950.0000.1980.4080.6130.4600.0000.0000.8020.1130.2580.5030.3570.0000.0000.3590.5550.3950.2210.0000.5310.4680.4470.1780.0000.0000.4130.0000.0000.000
championName0.0000.0000.0001.0001.0000.0000.0000.0000.3650.4920.1260.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0000.0000.0000.0000.6170.2110.4600.0000.0000.0000.0000.0000.6610.0000.0950.0000.1980.4080.6130.4600.0000.0000.8020.1130.2580.5030.3570.0000.0000.3590.5550.3950.2210.0000.5310.4680.4470.1780.0000.0000.4130.0000.0000.000
damageDealtToBuildings0.015-0.0630.4950.0000.0001.0000.7451.0000.1250.2480.2590.0000.0000.0000.4040.3420.1250.3120.0000.4050.332-0.0630.000-0.1710.000-0.1260.4100.7030.2690.4660.8400.2890.4190.1250.5160.4200.6940.0000.7870.4110.7200.1770.2080.5380.750-0.3070.3240.4290.661-0.4470.6760.8470.3670.4670.2740.000-0.258-0.3600.2210.7170.717-0.2170.1820.0000.0920.289
damageDealtToObjectives0.168-0.2240.3800.0000.0000.7451.0000.7450.1610.1920.0000.0000.0000.4380.3210.2360.0000.4210.0000.3660.366-0.2240.000-0.3400.000-0.3120.3290.5610.2970.0000.8400.1980.5840.0000.3250.3380.5400.0000.5680.4680.5620.2350.2090.2330.288-0.1840.3830.3480.513-0.3620.5340.6680.2110.3610.1990.000-0.107-0.078-0.0580.4110.661-0.2450.3030.5320.1210.198
damageDealtToTurrets0.015-0.0630.4950.0000.0001.0000.7451.0000.1250.2480.2590.0000.0000.0000.4040.3420.1250.3120.0000.4050.332-0.0630.000-0.1710.000-0.1260.4100.7030.2690.4660.8400.2890.4190.1250.5160.4200.6940.0000.7870.4110.7200.1770.2080.5380.750-0.3070.3240.4290.661-0.4470.6760.8470.3670.4670.2740.000-0.258-0.3600.2210.7170.717-0.2170.1820.0000.0920.289
damageSelfMitigated0.4650.1110.7360.3650.3650.1250.1610.1251.0000.6620.0000.1710.1710.4110.7980.7560.3800.2400.0000.4810.0000.1110.000-0.1520.0000.2590.7110.5670.6650.1710.0000.0000.3400.0000.4440.5790.5070.0910.3300.6600.5280.4220.2400.4130.1160.3780.8220.6910.5880.1530.6560.4610.5090.5470.6820.0000.6930.2570.5910.5490.1640.3410.6630.0000.7820.000
deaths-0.0340.1680.6190.4920.4920.2480.1920.2480.6621.0000.0000.1710.1710.4470.7710.8040.0000.0000.3070.6640.0000.1680.334-0.2720.000-0.1630.6260.5350.4470.0000.0000.0000.0000.0000.7390.4000.7570.2240.3290.7550.6440.7250.0000.7300.3160.0420.7510.8380.522-0.3500.7740.4950.6260.4860.9670.1710.4640.0690.6860.0000.1600.4630.5230.0000.6720.000
doubleKills0.0000.1850.4650.1260.1260.2590.0000.2590.0000.0001.0000.0000.0000.0000.0000.0000.0000.3460.0000.0000.3330.1850.0000.4970.9390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1880.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2950.3100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
eligibleForProgression0.0000.0000.8040.0000.0000.0000.0000.0000.1710.1710.0001.0000.0000.0000.8040.8040.0000.0000.0000.2970.0000.0000.3700.0000.5690.2970.0000.1710.0000.0000.0000.0000.0000.0000.3830.8040.0000.3700.0000.8040.1710.0000.5690.8400.0000.2970.3830.2970.8040.0000.2970.0000.8040.8400.0000.0000.0000.0000.0000.0000.0000.0000.7670.0000.8400.000
firstTowerAssist0.0000.1400.8040.0000.0000.0000.0000.0000.1710.1710.0000.0001.0000.0000.8040.2970.0000.0000.0000.8040.0000.1400.3700.0000.2100.2970.0000.0000.0000.0000.0000.0000.0000.1960.3830.0000.0000.3700.7280.0000.1710.0000.0000.3830.0000.0000.3830.2970.2970.3830.2970.0000.0000.3830.0000.0000.0000.3830.0000.0000.0000.1710.1710.0000.3830.000
gameEndedInSurrender0.3990.2240.0000.0000.0000.0000.4380.0000.4110.4470.0000.0000.0001.0000.2610.0000.0000.3970.3160.0000.0000.2240.1630.0000.0000.0000.3820.1290.0000.0000.0000.0000.0000.0000.2780.4130.3920.0000.2310.3640.2340.1120.0000.2980.0000.1730.2070.0000.5910.0000.0000.1380.2950.0000.0000.0000.3240.5360.1820.0000.2210.0000.0000.2970.0000.000
goldEarned0.3030.3000.8720.0000.0000.4040.3210.4040.7980.7710.0000.8040.8040.2611.0000.9590.2350.1860.0340.6260.2460.3000.233-0.0120.3280.0540.6840.6560.6000.3200.0000.0000.2130.4170.6770.6750.6700.3560.5700.7060.7950.5590.6240.6550.0000.1440.8420.7960.714-0.1600.8000.7140.6000.6260.7700.2970.4180.0720.6600.0820.4970.1420.6040.2500.7370.000
goldSpent0.2140.3560.8340.0000.0000.3420.2360.3420.7560.8040.0000.8040.2970.0000.9591.0000.2350.1860.1830.6640.0000.3560.4910.0050.3650.0330.5970.5540.5580.1360.0000.2750.0480.2350.7140.6860.6580.5820.5110.6910.7280.6160.2010.7280.0000.1890.7890.7900.659-0.1600.7680.6350.5720.5990.8170.2970.3840.1890.7130.0000.4800.2220.5870.4670.7340.275
inhibitorKills0.0000.4970.8040.0000.0000.1250.0000.1250.3800.0000.0000.0000.0000.0000.2350.2351.0000.7050.0000.0000.1440.4970.0000.5400.5830.0000.0000.0000.2930.0000.0000.0000.2230.0000.0000.0000.0000.0000.2930.0000.0000.1250.3780.0000.0000.2350.4820.0000.0000.3380.0000.0000.0000.0000.0000.1960.0000.0000.0000.5920.8400.0000.0000.0000.0000.000
inhibitorTakedowns0.0000.6710.6450.0000.0000.3120.4210.3120.2400.0000.3460.0000.0000.3970.1860.1860.7051.0000.2310.0000.0000.6710.0000.5230.6750.0000.0000.0000.0000.0000.0000.6340.8380.0000.0000.4480.2490.0000.0000.0000.0940.4560.1460.0000.0000.0000.2390.3240.2020.0000.3190.2460.0000.0000.0000.9700.0000.2780.0000.4170.6310.1490.0000.2360.0000.634
inhibitorsLost0.0000.0000.0000.0000.0000.0000.0000.0000.0000.3070.0000.0000.0000.3160.0340.1830.0000.2311.0000.0000.0000.0000.3940.0000.0000.0500.2660.0000.1220.0000.0000.5860.3210.0000.0000.0000.3020.4570.0000.0000.0000.0000.0000.0000.0000.2690.0000.0000.0000.2950.0000.0000.1900.0000.3970.0000.2990.0000.2840.0000.0000.4990.0000.0000.0000.586
itemsPurchased0.102-0.1180.7880.0000.0000.4050.3660.4050.4810.6640.0000.2970.8040.0000.6260.6640.0000.0000.0001.0000.000-0.1180.293-0.3110.0000.0000.7850.6390.4320.0000.0000.0000.0000.4510.6600.3640.6950.3610.7170.5900.7450.6320.4540.7490.347-0.0510.7660.7440.595-0.1730.7790.6850.5980.7530.6730.0000.243-0.0320.5450.3030.2780.2850.6420.2250.6590.000
killingSprees0.0000.4210.0000.1000.1000.3320.3660.3320.0000.0000.3330.0000.0000.0000.2460.0000.1440.0000.0000.0001.0000.4210.0000.5700.1440.2210.3510.3930.4050.0000.4430.0000.0000.0000.0670.0000.2040.0000.4080.0000.2180.0000.3010.0000.2890.0000.3240.2150.0000.0000.3490.4100.0000.0000.3860.0000.0000.0000.0000.3300.0670.0000.0000.0000.0000.000
kills0.2981.0000.0310.0000.000-0.063-0.224-0.0630.1110.1680.1850.0000.1400.2240.3000.3560.4970.6710.000-0.1180.4211.0000.0000.7830.5430.197-0.254-0.0510.0800.4130.0000.2020.3900.0000.0620.494-0.1270.000-0.1310.1070.0760.2730.0000.1950.3440.593-0.114-0.0210.2380.189-0.087-0.103-0.109-0.0680.1160.8740.2350.3590.1720.2840.225-0.1530.0200.0000.1710.202
lane0.0000.0000.3160.0000.0000.0000.0000.0000.0000.3340.0000.3700.3700.1630.2330.4910.0000.0000.3940.2930.0000.0001.0000.5830.1110.2970.0000.0000.0000.0810.0000.1630.0000.4380.2700.3880.2940.8860.2030.0000.0000.3320.4620.0000.0000.0000.3140.2150.0000.5830.3740.0000.4470.0000.4740.0000.0000.0000.0000.0000.0000.3330.1850.0000.2580.163
largestKillingSpree0.3220.783-0.1850.0000.000-0.171-0.340-0.171-0.152-0.2720.4970.0000.0000.000-0.0120.0050.5400.5230.000-0.3110.5700.7830.5831.0000.5420.423-0.373-0.316-0.1340.0000.0000.0000.0000.000-0.3470.172-0.4410.390-0.225-0.353-0.189-0.2080.000-0.1180.0000.474-0.341-0.392-0.0980.467-0.359-0.286-0.355-0.308-0.3080.8740.0230.240-0.0670.0000.093-0.246-0.1720.042-0.0650.000
largestMultiKill0.0000.5430.5920.0000.0000.0000.0000.0000.0000.0000.9390.5690.2100.0000.3280.3650.5830.6750.0000.0000.1440.5430.1110.5421.0000.0000.0000.0000.0000.0000.2100.0000.0340.3780.0000.2300.0000.1390.1900.0000.0000.0000.1040.2080.0000.0000.0000.0000.0000.0000.0000.1320.4590.0000.0000.9390.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
longestTimeSpentLiving0.4860.1970.1740.0000.000-0.126-0.312-0.1260.259-0.1630.0000.2970.2970.0000.0540.0330.0000.0000.0500.0000.2210.1970.2970.4230.0001.0000.168-0.0300.2400.0000.2970.0000.0000.000-0.0870.116-0.0940.1730.134-0.201-0.036-0.2520.212-0.0100.5140.3260.112-0.0450.0240.563-0.0080.0470.1060.098-0.1450.0000.2690.0310.1790.068-0.1670.3330.1790.2480.1870.000
magicDamageDealt0.180-0.2540.7970.6170.6170.4100.3290.4100.7110.6260.0000.0000.0000.3820.6840.5970.0000.0000.2660.7850.351-0.2540.000-0.3730.0000.1681.0000.8050.6050.0860.0000.0000.0000.0000.6020.2790.6740.0000.7020.6350.7720.3760.0000.5590.524-0.0950.8680.8250.625-0.1390.8180.7250.7470.7500.6430.0000.404-0.1880.4840.3930.1810.2280.5890.1940.7030.000
magicDamageDealtToChampions0.173-0.0510.7060.2110.2110.7030.5610.7030.5670.5350.0000.1710.0000.1290.6560.5540.0000.0000.0000.6390.393-0.0510.000-0.3160.000-0.0300.8051.0000.5370.3150.7670.2340.1710.0000.6510.5180.7180.0000.7860.7770.9040.4450.0000.5400.453-0.0720.6750.7610.902-0.3600.8190.8540.6230.7180.5410.0000.247-0.2600.3980.4100.465-0.0240.4820.0000.5330.234
magicDamageTaken0.2830.0800.4950.4600.4600.2690.2970.2690.6650.4470.0000.0000.0000.0000.6000.5580.2930.0000.1220.4320.4050.0800.000-0.1340.0000.2400.6050.5371.0000.2010.0000.0000.0000.0000.4420.4390.3670.0000.5260.3580.5630.3490.2330.3060.3500.0580.6610.5350.5670.0000.5650.4860.6810.5480.4940.0000.2750.0930.3260.5500.0880.0340.4420.0000.7660.000
neutralMinionsKilled0.2970.4130.1360.0000.0000.4660.0000.4660.1710.0000.0000.0000.0000.0000.3200.1360.0000.0000.0000.0000.0000.4130.0810.0000.0000.0000.0860.3150.2011.0000.0000.0000.0000.5190.0860.2310.0860.0960.5000.2310.3150.3350.0000.0000.0000.0000.0000.0000.2970.0000.3510.0000.0000.0000.0000.0000.0000.0000.0000.0000.5360.0000.2080.1130.0000.000
nexusKills0.0000.0000.0000.0000.0000.8400.8400.8400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4430.0000.0000.0000.2100.2970.0000.7670.0000.0001.0000.0000.0000.0000.0000.0000.8400.0000.7280.0000.0000.0000.2100.8400.0000.0000.0000.0000.8040.0000.0000.8740.2970.0000.0000.0000.0000.0000.0000.5140.8400.0000.0000.0000.0000.000
nexusLost0.3260.2020.1930.0000.0000.2890.1980.2890.0000.0000.0000.0000.0000.0000.0000.2750.0000.6340.5860.0000.0000.2020.1630.0000.0000.0000.0000.2340.0000.0000.0001.0000.6580.0000.0000.3640.4150.0000.4530.0000.0000.5060.1190.0000.0000.2210.0000.0000.2610.1420.4770.2610.2210.0000.0000.0000.4610.1420.0000.4280.5780.5670.0000.0000.0000.885
nexusTakedowns0.1910.3900.3620.0000.0000.4190.5840.4190.3400.0000.0000.0000.0000.0000.2130.0480.2230.8380.3210.0000.0000.3900.0000.0000.0340.0000.0000.1710.0000.0000.0000.6581.0000.0000.0000.4890.5250.0000.3160.0480.2690.6360.4380.0000.0000.0000.0000.1040.4750.0000.4170.5680.0000.3720.0000.0000.4570.0000.0000.7170.8400.1710.0000.0000.0000.658
objectivesStolen0.5030.0000.3920.0000.0000.1250.0000.1250.0000.0000.0000.0000.1960.0000.4170.2350.0000.0000.0000.4510.0000.0000.4380.0000.3780.0000.0000.0000.0000.5190.0000.0000.0001.0000.1250.0000.1250.4380.7280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4510.0000.1250.0000.0000.0000.0000.0000.3380.0000.0000.0000.0000.000
physicalDamageDealt-0.1950.0620.6780.6610.6610.5160.3250.5160.4440.7390.0000.3830.3830.2780.6770.7140.0000.0000.0000.6600.0670.0620.270-0.3470.000-0.0870.6020.6510.4420.0860.0000.0000.0000.1251.0000.4790.8420.4410.6580.7180.7680.6870.1690.7880.750-0.2650.6190.9050.642-0.5980.7910.7320.8400.5650.8010.0000.126-0.1200.5400.2690.2990.2440.3320.0000.4510.000
physicalDamageDealtToChampions0.5190.4940.6180.0000.0000.4200.3380.4200.5790.4000.0000.8040.0000.4130.6750.6860.0000.4480.0000.3640.0000.4940.3880.1720.2300.1160.2790.5180.4390.2310.0000.3640.4890.0000.4791.0000.4790.0530.5110.5210.5560.5810.3050.5980.0000.3760.4640.4580.8160.1210.5460.5400.2180.3690.4810.2970.3810.4020.3670.0000.552-0.1090.3490.4800.4800.364
physicalDamageTaken-0.004-0.1270.7340.0950.0950.6940.5400.6940.5070.7570.1880.0000.0000.3920.6700.6580.0000.2490.3020.6950.204-0.1270.294-0.4410.000-0.0940.6740.7180.3670.0860.8400.4150.5250.1250.8420.4791.0000.2040.7050.7110.8110.5850.0000.8040.362-0.2840.6880.8420.672-0.4780.9400.8320.6680.5030.7910.0000.111-0.1950.5390.3730.4040.3580.5070.0000.4360.415
role0.0000.0000.3070.0000.0000.0000.0000.0000.0910.2240.0000.3700.3700.0000.3560.5820.0000.0000.4570.3610.0000.0000.8860.3900.1390.1730.0000.0000.0000.0960.0000.0000.0000.4380.4410.0530.2041.0000.2300.0000.0000.3270.5290.0000.0000.0000.4160.3020.0000.4670.2840.0000.3910.0000.5190.0000.0000.0000.0000.0000.0000.2980.3320.2610.2110.000
spell1Casts0.180-0.1310.7580.1980.1980.7870.5680.7870.3300.3290.0000.0000.7280.2310.5700.5110.2930.0000.0000.7170.408-0.1310.203-0.2250.1900.1340.7020.7860.5260.5000.7280.4530.3160.7280.6580.5110.7050.2301.0000.4330.8490.3840.3180.6630.441-0.2740.6140.6250.763-0.2540.7820.9210.6320.7100.4080.000-0.047-0.2480.3460.3660.533-0.1050.3980.0000.4490.453
spell2Casts0.0790.1070.6610.4080.4080.4110.4680.4110.6600.7550.1560.8040.0000.3640.7060.6910.0000.0000.0000.5900.0000.1070.000-0.3530.000-0.2010.6350.7770.3580.2310.0000.0000.0480.0000.7180.5210.7110.0000.4331.0000.7400.6640.2120.5380.0000.1700.6640.8460.728-0.4510.7170.6250.5490.6170.7400.0000.4760.0920.4530.3990.4380.1860.5460.0000.5090.000
spell3Casts0.1250.0760.7960.6130.6130.7200.5620.7200.5280.6440.0000.1710.1710.2340.7950.7280.0000.0940.0000.7450.2180.0760.000-0.1890.000-0.0360.7720.9040.5630.3150.0000.0000.2690.0000.7680.5560.8110.0000.8490.7401.0000.5570.1440.6780.767-0.1670.7110.8190.863-0.4380.8960.9070.7140.6840.6520.0000.098-0.2360.4600.5140.5320.0280.5670.0000.5970.000
spell4Casts0.1110.2730.5200.4600.4600.1770.2350.1770.4220.7250.0000.0000.0000.1120.5590.6160.1250.4560.0000.6320.0000.2730.332-0.2080.000-0.2520.3760.4450.3490.3350.0000.5060.6360.0000.6870.5810.5850.3270.3840.6640.5571.0000.3350.6750.0000.1850.5320.6540.580-0.2610.5490.3790.4950.4430.7710.3830.4230.3740.4670.1880.1680.1610.4770.0000.6040.506
summoner1Casts0.0000.0000.4710.0000.0000.2080.2090.2080.2400.0000.0000.5690.0000.0000.6240.2010.3780.1460.0000.4540.3010.0000.4620.0000.1040.2120.0000.0000.2330.0000.2100.1190.4380.0000.1690.3050.0000.5290.3180.2120.1440.3351.0000.3670.4250.0000.3600.1900.0000.0000.1050.1040.0420.3920.3550.2100.0000.0000.0000.3590.0600.0000.1730.0000.1740.119
summoner2Casts0.0440.1950.7200.0000.0000.5380.2330.5380.4130.7300.0000.8400.3830.2980.6550.7280.0000.0000.0000.7490.0000.1950.000-0.1180.208-0.0100.5590.5400.3060.0000.8400.0000.0000.0000.7880.5980.8040.0000.6630.5380.6780.6750.3671.0000.042-0.0500.6010.7360.633-0.2090.7870.6610.5380.5280.7890.0000.2140.0650.5930.3140.4100.1990.3470.0000.4910.000
summoner2Id0.1840.3440.0000.8020.8020.7500.2880.7500.1160.3160.0000.0000.0000.0000.0000.0000.0000.0000.0000.3470.2890.3440.0000.0000.0000.5140.5240.4530.3500.0000.0000.0000.0000.0000.7500.0000.3620.0000.4410.0000.7670.0000.4250.0421.0000.5230.5330.1220.0000.2590.4100.7880.3980.0000.0000.0000.0000.3270.0000.5790.5890.0000.0000.2370.0000.000
timeCCingOthers0.6560.5930.0980.1130.113-0.307-0.184-0.3070.3780.0420.0000.2970.0000.1730.1440.1890.2350.0000.269-0.0510.0000.5930.0000.4740.0000.326-0.095-0.0720.0580.0000.0000.2210.0000.000-0.2650.376-0.2840.000-0.2740.170-0.1670.1850.000-0.0500.5231.0000.073-0.1120.1100.613-0.191-0.260-0.3270.1190.0050.0000.6770.6850.2070.0000.1280.0120.3180.3450.3180.221
timePlayed0.335-0.1140.8630.2580.2580.3240.3830.3240.8220.7510.0000.3830.3830.2070.8420.7890.4820.2390.0000.7660.324-0.1140.314-0.3410.0000.1120.8680.6750.6610.0000.0000.0000.0000.0000.6190.4640.6880.4160.6140.6640.7110.5320.3600.6010.5330.0731.0000.8230.609-0.0550.8340.6990.6540.7230.7720.0000.5460.0710.6590.4170.3020.3120.6850.3690.8290.000
totalDamageDealt-0.008-0.0210.7950.5030.5030.4290.3480.4290.6910.8380.0000.2970.2970.0000.7960.7900.0000.3240.0000.7440.215-0.0210.215-0.3920.000-0.0450.8250.7610.5350.0000.0000.0000.1040.0000.9050.4580.8420.3020.6250.8460.8190.6540.1900.7360.122-0.1120.8231.0000.679-0.4370.8770.7300.8370.6420.8770.0000.365-0.0440.5720.0000.2920.2990.5370.0000.6550.000
totalDamageDealtToChampions0.3290.2380.7050.3570.3570.6610.5130.6610.5880.5220.0000.8040.2970.5910.7140.6590.0000.2020.0000.5950.0000.2380.000-0.0980.0000.0240.6250.9020.5670.2970.8040.2610.4750.0000.6420.8160.6720.0000.7630.7280.8630.5800.0000.6330.0000.1100.6090.6791.000-0.1930.7740.8020.4960.6290.5560.0000.293-0.0090.3980.3180.536-0.0870.4480.0000.5600.261
totalDamageShieldedOnTeammates0.7120.189-0.0690.0000.000-0.447-0.362-0.4470.153-0.3500.0000.0000.3830.000-0.160-0.1600.3380.0000.295-0.1730.0000.1890.5830.4670.0000.563-0.139-0.3600.0000.0000.0000.1420.0000.000-0.5980.121-0.4780.467-0.254-0.451-0.438-0.2610.000-0.2090.2590.613-0.055-0.437-0.1931.000-0.329-0.401-0.493-0.204-0.3300.3830.3780.465-0.0720.000-0.1980.0140.0960.3280.1560.142
totalDamageTaken0.144-0.0870.8320.0000.0000.6760.5340.6760.6560.7740.0000.2970.2970.0000.8000.7680.0000.3190.0000.7790.349-0.0870.374-0.3590.000-0.0080.8180.8190.5650.3510.0000.4770.4170.0000.7910.5460.9400.2840.7820.7170.8960.5490.1050.7870.410-0.1910.8340.8770.774-0.3291.0000.8790.7020.6130.8030.0000.198-0.1670.5960.5260.4370.2940.6110.0000.6470.477
totalHeal0.138-0.1030.8150.3590.3590.8470.6680.8470.4610.4950.2950.0000.0000.1380.7140.6350.0000.2460.0000.6850.410-0.1030.000-0.2860.1320.0470.7250.8540.4860.0000.8740.2610.5680.0000.7320.5400.8320.0000.9210.6250.9070.3790.1040.6610.788-0.2600.6990.7300.802-0.4010.8791.0000.6350.7180.5230.0000.032-0.3250.4580.5150.6310.0260.4500.0000.4150.261
totalMinionsKilled-0.185-0.1090.5980.5550.5550.3670.2110.3670.5090.6260.3100.8040.0000.2950.6000.5720.0000.0000.1900.5980.000-0.1090.447-0.3550.4590.1060.7470.6230.6810.0000.2970.2210.0000.4510.8400.2180.6680.3910.6320.5490.7140.4950.0420.5380.398-0.3270.6540.8370.496-0.4930.7020.6351.0000.6010.6890.0000.140-0.2160.4530.0000.0130.2360.3620.0000.5740.221
totalTimeCCDealt0.142-0.0680.7700.3950.3950.4670.3610.4670.5470.4860.0000.8400.3830.0000.6260.5990.0000.0000.0000.7530.000-0.0680.000-0.3080.0000.0980.7500.7180.5480.0000.0000.0000.3720.0000.5650.3690.5030.0000.7100.6170.6840.4430.3920.5280.0000.1190.7230.6420.629-0.2040.6130.7180.6011.0000.4870.0000.366-0.0640.5450.2870.4150.0170.4940.0000.5660.000
totalTimeSpentDead-0.0340.1160.6540.2210.2210.2740.1990.2740.6820.9670.0000.0000.0000.0000.7700.8170.0000.0000.3970.6730.3860.1160.474-0.3080.000-0.1450.6430.5410.4940.0000.0000.0000.0000.1250.8010.4810.7910.5190.4080.7400.6520.7710.3550.7890.0000.0050.7720.8770.556-0.3300.8030.5230.6890.4871.0000.0000.4610.1750.6740.0000.1730.4070.4820.4430.6990.000
tripleKills0.0000.8740.8040.0000.0000.0000.0000.0000.0000.1710.0000.0000.0000.0000.2970.2970.1960.9700.0000.0000.0000.8740.0000.8740.9390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2970.0000.0000.0000.0000.0000.3830.2100.0000.0000.0000.0000.0000.0000.3830.0000.0000.0000.0000.0001.0000.0000.0000.1400.0000.3830.1710.0000.0000.0000.000
trueDamageDealt0.4820.2350.3640.5310.531-0.258-0.107-0.2580.6930.4640.0000.0000.0000.3240.4180.3840.0000.0000.2990.2430.0000.2350.0000.0230.0000.2690.4040.2470.2750.0000.0000.4610.4570.0000.1260.3810.1110.000-0.0470.4760.0980.4230.0000.2140.0000.6770.5460.3650.2930.3780.1980.0320.1400.3660.4610.0001.0000.5250.4110.000-0.0510.2510.3350.1130.5450.461
trueDamageDealtToChampions0.3930.3590.0120.4680.468-0.360-0.078-0.3600.2570.0690.0000.0000.3830.5360.0720.1890.0000.2780.000-0.0320.0000.3590.0000.2400.0000.031-0.188-0.2600.0930.0000.0000.1420.0000.000-0.1200.402-0.1950.000-0.2480.092-0.2360.3740.0000.0650.3270.6850.071-0.044-0.0090.465-0.167-0.325-0.216-0.0640.1750.0000.5251.000-0.0070.0000.087-0.1110.1080.6580.2880.142
trueDamageTaken0.1290.1720.6390.4470.4470.221-0.0580.2210.5910.6860.0000.0000.0000.1820.6600.7130.0000.0000.2840.5450.0000.1720.000-0.0670.0000.1790.4840.3980.3260.0000.0000.0000.0000.0000.5400.3670.5390.0000.3460.4530.4600.4670.0000.5930.0000.2070.6590.5720.398-0.0720.5960.4580.4530.5450.6740.1400.411-0.0071.0000.0000.1680.5490.5400.0000.6340.000
turretKills0.0000.2840.3250.1780.1780.7170.4110.7170.5490.0000.0000.0000.0000.0000.0820.0000.5920.4170.0000.3030.3300.2840.0000.0000.0000.0680.3930.4100.5500.0000.5140.4280.7170.0000.2690.0000.3730.0000.3660.3990.5140.1880.3590.3140.5790.0000.4170.0000.3180.0000.5260.5150.0000.2870.0000.0000.0000.0000.0001.0000.6320.1520.0000.0000.0000.428
turretTakedowns0.2700.2250.5250.0000.0000.7170.6610.7170.1640.1600.0000.0000.0000.2210.4970.4800.8400.6310.0000.2780.0670.2250.0000.0930.000-0.1670.1810.4650.0880.5360.8400.5780.8400.3380.2990.5520.4040.0000.5330.4380.5320.1680.0600.4100.5890.1280.3020.2920.536-0.1980.4370.6310.0130.4150.1730.383-0.0510.0870.1680.6321.000-0.4010.2310.0000.1050.578
turretsLost-0.028-0.1530.1760.0000.000-0.217-0.245-0.2170.3410.4630.0000.0000.1710.0000.1420.2220.0000.1490.4990.2850.000-0.1530.333-0.2460.0000.3330.228-0.0240.0340.0000.0000.5670.1710.0000.244-0.1090.3580.298-0.1050.1860.0280.1610.0000.1990.0000.0120.3120.299-0.0870.0140.2940.0260.2360.0170.4070.1710.251-0.1110.5490.152-0.4011.0000.4340.3160.2620.567
visionScore0.5500.0200.7070.4130.4130.1820.3030.1820.6630.5230.0000.7670.1710.0000.6040.5870.0000.0000.0000.6420.0000.0200.185-0.1720.0000.1790.5890.4820.4420.2080.0000.0000.0000.0000.3320.3490.5070.3320.3980.5460.5670.4770.1730.3470.0000.3180.6850.5370.4480.0960.6110.4500.3620.4940.4820.0000.3350.1080.5400.0000.2310.4341.0000.4490.7470.000
wardsKilled0.5040.0000.0000.0000.0000.0000.5320.0000.0000.0000.0000.0000.0000.2970.2500.4670.0000.2360.0000.2250.0000.0000.0000.0420.0000.2480.1940.0000.0000.1130.0000.0000.0000.0000.0000.4800.0000.2610.0000.0000.0000.0000.0000.0000.2370.3450.3690.0000.0000.3280.0000.0000.0000.0000.4430.0000.1130.6580.0000.0000.0000.3160.4491.0000.4510.000
wardsPlaced0.4500.1710.6790.0000.0000.0920.1210.0920.7820.6720.0000.8400.3830.0000.7370.7340.0000.0000.0000.6590.0000.1710.258-0.0650.0000.1870.7030.5330.7660.0000.0000.0000.0000.0000.4510.4800.4360.2110.4490.5090.5970.6040.1740.4910.0000.3180.8290.6550.5600.1560.6470.4150.5740.5660.6990.0000.5450.2880.6340.0000.1050.2620.7470.4511.0000.000
win0.3260.2020.1930.0000.0000.2890.1980.2890.0000.0000.0000.0000.0000.0000.0000.2750.0000.6340.5860.0000.0000.2020.1630.0000.0000.0000.0000.2340.0000.0000.0000.8850.6580.0000.0000.3640.4150.0000.4530.0000.0000.5060.1190.0000.0000.2210.0000.0000.2610.1420.4770.2610.2210.0000.0000.0000.4610.1420.0000.4280.5780.5670.0000.0000.0001.000

Missing values

2025-03-06T10:38:37.915448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-06T10:38:38.387018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

assistsbaronKillsbountyLevelchampLevelchampionIdchampionNamedamageDealtToBuildingsdamageDealtToObjectivesdamageDealtToTurretsdamageSelfMitigateddeathsdetectorWardsPlaceddoubleKillsdragonKillseligibleForProgressionfirstBloodAssistfirstBloodKillfirstTowerAssistfirstTowerKillgameEndedInEarlySurrendergameEndedInSurrendergoldEarnedgoldSpentinhibitorKillsinhibitorTakedownsinhibitorsLostitemsPurchasedkillingSpreeskillslanelargestKillingSpreelargestMultiKilllongestTimeSpentLivingmagicDamageDealtmagicDamageDealtToChampionsmagicDamageTakenneutralMinionsKillednexusKillsnexusLostnexusTakedownsobjectivesStolenobjectivesStolenAssistspentaKillsphysicalDamageDealtphysicalDamageDealtToChampionsphysicalDamageTakenplacementquadraKillsrolesightWardsBoughtInGamespell1Castsspell2Castsspell3Castsspell4CastssubteamPlacementsummoner1Castssummoner1Idsummoner2Castssummoner2IdteamEarlySurrenderedteamPositiontimeCCingOtherstimePlayedtotalAllyJungleMinionsKilledtotalDamageDealttotalDamageDealtToChampionstotalDamageShieldedOnTeammatestotalDamageTakentotalEnemyJungleMinionsKilledtotalHealtotalHealsOnTeammatestotalMinionsKilledtotalTimeCCDealttotalTimeSpentDeadtotalUnitsHealedtripleKillstrueDamageDealttrueDamageDealtToChampionstrueDamageTakenturretKillsturretTakedownsturretsLostunrealKillsvisionClearedPingsvisionScorevisionWardsBoughtInGamewardsKilledwardsPlacedwin
025051698Shen1291139751291396374000TrueFalseFalseFalseFalseFalseFalse1003097500102115TOP21609223511030013465000100036064164611555000SOLO011232488031234FalseTOP631897069187286232489301980304509528013210107711862118214500190410True
19011498Shen135758021357275394000TrueFalseFalseFalseFalseFalseFalse780674000031501TOP016471358435441063100100002788466481809900DUO06216285021224FalseTOP22161904486810551162029696020970991539310339935896400120017027False
26031198Shen14914996149182890010TrueFalseFalseTrueFalseFalseTrue57634700000813NONE3208105386717434000100191785224612400SUPPORT04612163031214FalseTOP239660281599482689835201131092149010875389485010008005True
321051898Shen9272634927882288000TrueFalseFalseFalseFalseFalseFalse12613127500032115TOP319544616989329552001000039127116553425900SOLO09932497031264FalseTOP462547011847321686398250385028610128243317103317610986573021100250015False
49061498Shen627627627407014010TrueFalseFalseFalseFalseFalseFalse930388500011416JUNGLE4290918689924511948001000029112128041288000NONE06624387021224FalseTOP531565059259231012248261260183201262091421011456105112970080015009False
58051298Shen370150353701175431000TrueFalseFalseFalseFalseFalseFalse77494750110625NONE3180310181598969290001000244277194856900SUPPORT07119303011224FalseTOP31116303964414116663156320259409324828105035932133250006004True
6100898Shen00023980000FalseFalseFalseFalseFalseFalseTrue32922200000400NONE000249411500001000089801409284300SUPPORT027492011204FalseTOP68390115582643346284301560558801082820003001001False
711031598Shen53350553301132000TrueFalseFalseFalseFalseFalseTrue940186500001313TOP3116202288945291205100100003543583981106300SOLO08614314031214FalseTOP21178006714513420229523380028340175203491088204922660070012018False
814061598Shen544622544230411000TrueFalseFalseFalseFalseFalseTrue895580300001616MIDDLE511094207275596119930000000348128516641800DUO010511377021234FalseTOP30152605918314979317618567017930140222381036438661550020016029True
99011698Shen231523152315441044000TrueFalseFalseFalseFalseFalseFalse920287000011701TOP011190426421142415476001000041108112111807400SOLO011717345031244FalseTOP261897010090422987267138417032470176297143101715335248670090013009False
assistsbaronKillsbountyLevelchampLevelchampionIdchampionNamedamageDealtToBuildingsdamageDealtToObjectivesdamageDealtToTurretsdamageSelfMitigateddeathsdetectorWardsPlaceddoubleKillsdragonKillseligibleForProgressionfirstBloodAssistfirstBloodKillfirstTowerAssistfirstTowerKillgameEndedInEarlySurrendergameEndedInSurrendergoldEarnedgoldSpentinhibitorKillsinhibitorTakedownsinhibitorsLostitemsPurchasedkillingSpreeskillslanelargestKillingSpreelargestMultiKilllongestTimeSpentLivingmagicDamageDealtmagicDamageDealtToChampionsmagicDamageTakenneutralMinionsKillednexusKillsnexusLostnexusTakedownsobjectivesStolenobjectivesStolenAssistspentaKillsphysicalDamageDealtphysicalDamageDealtToChampionsphysicalDamageTakenplacementquadraKillsrolesightWardsBoughtInGamespell1Castsspell2Castsspell3Castsspell4CastssubteamPlacementsummoner1Castssummoner1Idsummoner2Castssummoner2IdteamEarlySurrenderedteamPositiontimeCCingOtherstimePlayedtotalAllyJungleMinionsKilledtotalDamageDealttotalDamageDealtToChampionstotalDamageShieldedOnTeammatestotalDamageTakentotalEnemyJungleMinionsKilledtotalHealtotalHealsOnTeammatestotalMinionsKilledtotalTimeCCDealttotalTimeSpentDeadtotalUnitsHealedtripleKillstrueDamageDealttrueDamageDealtToChampionstrueDamageTakenturretKillsturretTakedownsturretsLostunrealKillsvisionClearedPingsvisionScorevisionWardsBoughtInGamewardsKilledwardsPlacedwin
99011698Shen231523152315441044000TrueFalseFalseFalseFalseFalseFalse920287000011701TOP011190426421142415476001000041108112111807400SOLO011717345031244FalseTOP261897010090422987267138417032470176297143101715335248670090013009False
10130121798Shen267527082675398903010TrueFalseFalseFalseFalseFalseFalse119211065013215112TOP11311041798678738905000100037594136281577300SOLO010422454041234FalseTOP331683059712217161748292720350801202653911413121445931570016027True
1114041836DrMundo137731671213773621446000TrueFalseFalseFalseFalseFalseFalse13430114500101604TOP015584032622675254724001000135925249035305600SOLO0171431179031244FalseTOP2321790182094483240831690209570199274228105842746463938300220212True
12006146Urgot000231246010TrueFalseFalseFalseFalseFalseTrue911590800021516TOP22589004180001000093346110971655700SOLO024252012021244FalseTOP271527010176912376022078048401552032221084231279134100100010007False
137001636DrMundo415156884151268493000TrueFalseFalseFalseFalseFalseFalse905181000011900JUNGLE0010694169115503476740101007330475963686500NONE014933795021236FalseTOP1716940118335230990426570121820181250861033390102411100017027False
142021636DrMundo392341103923203795000TrueFalseFalseFalseFalseFalseTrue949492000022312TOP216653355488221068800100006643957091835600SOLO016916657031246FalseTOP9181601004681456003977301135701932881451047428107281190016019False
155021836DrMundo109411558610941484336000TrueFalseFalseFalseFalseFalseFalse12354119500102902TOP014214821614208219870001000138251141444836500SOLO0173391048041266FalseTOP171971019103229338071366020032020839724710456498510134420015009True
168051636DrMundo115611199111561245146010TrueFalseFalseFalseFalseFalseTrue1040689000001925TOP2238439133186274532010100071094123483937900SOLO013933818041256FalseTOP2017530116789309750450740158240138258204106561011623330013017True
175061536DrMundo444744474447293446000TrueFalseFalseFalseFalseFalseFalse10742102000021626TOP31678313451299728719001000085864116461902300SOLO011423805041246FalseTOP13170601173992464404896509326019521220810189012221180013019False
186031654Malphite2333255233723127000TrueFalseFalseFalseFalseFalseTrue1080897500021903TOP0160288048135071733700100004179040871641300SOLO06863637021224FalseTOP2920630151342177250362170284802005482181021503130246600900200111False